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Refactoring to Brutex Network modules layout and introduced gradle build system 

Signed-off-by: brian <brian@brutex.de>
master
Brian Rosenberger 2022-09-20 15:40:53 +02:00
parent b58f5400a4
commit a002461812
8886 changed files with 244775 additions and 638526 deletions
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2
.github/workflows/build-deploy-linux-cuda-11.0.yml vendored
View File

@ -64,6 +64,6 @@ jobs:
nvcc --version
sudo apt-get autoremove
sudo apt-get clean
mvn -Possrh -Djavacpp.platform=linux-x86_64 -Dlibnd4j.compute="5.0 5.2 5.3 6.0 6.2 8.0" -Dlibnd4j.chip=cuda -pl ":nd4j-cuda-11.0,:deeplearning4j-cuda-11.0,:libnd4j" --also-make -Pcuda clean --batch-mode deploy -DskipTests
mvn -Possrh -Djavacpp.platform=linux-x86_64 -Dlibnd4j.compute="5.0 5.2 5.3 6.0 6.2 8.0" -Dlibnd4j.chip=cuda -pl ":nd4j-cuda-11.2,:deeplearning4j-cuda-11.2,:libnd4j" --also-make -Pcuda clean --batch-mode deploy -DskipTests

2
.github/workflows/build-deploy-windows-cuda-11.0.yml vendored
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@ -54,6 +54,6 @@ jobs:
dir "%CUDA_PATH%\lib"
set "PATH=C:\msys64\usr\bin;C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0\bin;C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0\lib\x64;%PATH%"
echo "Running cuda build"
mvn -Possrh -Djavacpp.platform=windows-x86_64 -Dlibnd4j.compute="5.0 5.2 5.3 6.0 6.2 8.0" -Djavacpp.platform=windows-x86_64 -pl ":nd4j-cuda-11.0,:deeplearning4j-cuda-11.0,:libnd4j" --also-make -Dlibnd4j.platform=windows-x86_64 -Pcuda -Dlibnd4j.chip=cuda -Pcuda clean --batch-mode deploy -DskipTests
mvn -Possrh -Djavacpp.platform=windows-x86_64 -Dlibnd4j.compute="5.0 5.2 5.3 6.0 6.2 8.0" -Djavacpp.platform=windows-x86_64 -pl ":nd4j-cuda-11.2,:deeplearning4j-cuda-11.2,:libnd4j" --also-make -Dlibnd4j.platform=windows-x86_64 -Pcuda -Dlibnd4j.chip=cuda -Pcuda clean --batch-mode deploy -DskipTests

2
.github/workflows/build-deploy-windows.yml vendored
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@ -16,7 +16,7 @@ jobs:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
windows-x86_64:
needs: pre-ci
runs-on: windows-2016
runs-on: windows-2019
steps:
- name: Cancel Previous Runs
uses: styfle/cancel-workflow-action@0.8.0

8
.github/workflows/cpu-integration-tests.yaml vendored
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@ -31,7 +31,7 @@ jobs:
protoc --version
cd dl4j-test-resources-master && mvn clean install -DskipTests && cd ..
export OMP_NUM_THREADS=1
mvn -Pintegration-tests -Ptestresources -Dlibnd4j.buildthreads=1 -Pnd4j-tests-cpu -Dlibnd4j.chip=cpu clean test
mvn -Pintegration-tests -Ptestresources -Dlibnd4j.buildthreads=1 -Ptest-nd4j-native -Dlibnd4j.chip=cpu clean test
windows-x86_64:
runs-on: windows-2019
@ -39,12 +39,12 @@ jobs:
- uses: actions/checkout@v2
- uses: ./.github/actions/msys2-base-setup
- uses: ./.github/actions/download-dl4j-test-resources-windows
- name: Run testsLossOpValidation
- name: Run tests
shell: cmd
run: |
set "PATH=C:\msys64\usr\bin;%PATH%"
export OMP_NUM_THREADS=1
mvn -DskipTestResourceEnforcement=true -Pintegration-tests -Ptestresources -Dlibnd4j.buildthreads=1 -Dlibnd4j.build="Debug" -Djavacpp.platform=windows-x86_64 -libnd4j.platform=windows-x86_64 -Pnd4j-tests-cpu -Dlibnd4j.chip=cpu clean test
mvn -DskipTestResourceEnforcement=true -Pintegration-tests -Ptestresources -Dlibnd4j.buildthreads=1 -Dlibnd4j.build="Debug" -Djavacpp.platform=windows-x86_64 -libnd4j.platform=windows-x86_64 -Ptest-nd4j-native -Dlibnd4j.chip=cpu clean test
@ -60,5 +60,5 @@ jobs:
run: |
brew install unzip ccache gcc swig autoconf-archive automake cmake libomp libtool libusb ant maven nasm xz pkg-config sdl gpg1 bison flex perl ragel binutils gradle gmp isl libmpc mpfr wget python
export OMP_NUM_THREADS=1
mvn -Pintegration-tests -Dlibnd4j.build="Debug" -Dlibnd4j.buildthreads=1 -Ptestresources -Djavacpp.platform=macosx-x86_64 -libnd4j.platform=macosx-x86_64 -Pnd4j-tests-cpu -Dlibnd4j.chip=cpu clean test
mvn -Pintegration-tests -Dlibnd4j.build="Debug" -Dlibnd4j.buildthreads=1 -Ptestresources -Djavacpp.platform=macosx-x86_64 -libnd4j.platform=macosx-x86_64 -Ptest-nd4j-native -Dlibnd4j.chip=cpu clean test

6
.github/workflows/cpu-sanity-check-tests.yaml vendored
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@ -31,7 +31,7 @@ jobs:
protoc --version
cd dl4j-test-resources-master && mvn clean install -DskipTests && cd ..
export OMP_NUM_THREADS=1
mvn -Ptestresources -pl ":deeplearning4j-modelimport,:deeplearning4j-core,:nd4j-native,:samediff-import,:libnd4j" -DexcludedGroups="long-running-tests,large-resources" -Dlibnd4j.buildthreads=1 -Pnd4j-tests-cpu -Dlibnd4j.chip=cpu clean test --fail-never
mvn -Ptestresources -pl ":deeplearning4j-modelimport,:deeplearning4j-core,:nd4j-native,:samediff-import,:libnd4j" -Dlibnd4j.buildthreads=1 -Ptest-nd4j-native -Dlibnd4j.chip=cpu clean test
windows-x86_64:
runs-on: windows-2019
@ -44,7 +44,7 @@ jobs:
run: |
set "PATH=C:\msys64\usr\bin;%PATH%"
export OMP_NUM_THREADS=1
mvn -pl ":deeplearning4j-modelimport,:deeplearning4j-core,:nd4j-native,:samediff-import,:libnd4j" -DexcludedGroups="long-running-tests,large-resources" -DskipTestResourceEnforcement=true -Ptestresources -Dlibnd4j.buildthreads=1 -Dlibnd4j.build="Debug" -Djavacpp.platform=windows-x86_64 -libnd4j.platform=windows-x86_64 -Pnd4j-tests-cpu -Dlibnd4j.chip=cpu clean test --fail-never
mvn -pl ":deeplearning4j-modelimport,:deeplearning4j-core,:nd4j-native,:samediff-import,:libnd4j" -DskipTestResourceEnforcement=true -Ptestresources -Dlibnd4j.buildthreads=1 -Dlibnd4j.build="Debug" -Djavacpp.platform=windows-x86_64 -libnd4j.platform=windows-x86_64 -Ptest-nd4j-native -Dlibnd4j.chip=cpu clean test
@ -60,5 +60,5 @@ jobs:
run: |
brew install unzip ccache gcc swig autoconf-archive automake cmake libomp libtool libusb ant maven nasm xz pkg-config sdl gpg1 bison flex perl ragel binutils gradle gmp isl libmpc mpfr wget python
export OMP_NUM_THREADS=1
mvn -pl ":deeplearning4j-modelimport,:deeplearning4j-core,:nd4j-native,:samediff-import,:libnd4j" -Dlibnd4j.build="Debug" -Dlibnd4j.buildthreads=1 -Ptestresources -Djavacpp.platform=macosx-x86_64 -libnd4j.platform=macosx-x86_64 -Pnd4j-tests-cpu -Dlibnd4j.chip=cpu clean test
mvn -pl ":deeplearning4j-modelimport,:deeplearning4j-core,:nd4j-native,:samediff-import,:libnd4j" -Dlibnd4j.build="Debug" -Dlibnd4j.buildthreads=1 -Ptestresources -Djavacpp.platform=macosx-x86_64 -libnd4j.platform=macosx-x86_64 -Ptest-nd4j-native -Dlibnd4j.chip=cpu clean test

29
.github/workflows/run-cpu-integration-tests-self-hosted.yml vendored
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@ -1,29 +0,0 @@
on:
workflow_dispatch:
jobs:
linux-x86_64:
runs-on: [self-hosted]
steps:
- uses: AutoModality/action-clean@v1
- name: Cancel Previous Runs
uses: styfle/cancel-workflow-action@0.8.0
with:
access_token: ${{ github.token }}
- uses: actions/checkout@v2
- uses: ./.github/actions/download-dl4j-test-resources-linux
- name: Run cpu tests
shell: bash
env:
DEBIAN_FRONTEND: noninteractive
run: |
export PATH="/opt/protobuf/bin:/usr/local/cuda-11.2/bin:$PATH"
nvcc --version
mvn --version
cmake --version
protoc --version
export OMP_NUM_THREADS=1
mkdir -p ${GITHUB_WORKSPACE}/resources
mkdir -p ${GITHUB_WORKSPACE}/cache
mvn -Dorg.nd4j.strumpf.resource.dirs=${GITHUB_WORKSPACE}/resources -Dorg.nd4j.test.resources.cache.dir=${GITHUB_WORKSPACE}/cache -DexcludedGroups="long-running-tests, large-resources, distributed-systems" -DskipTestResourceEnforcement=true -Ptestresources -Pintegration-tests -Pnd4j-tests-cpu clean test --fail-never
mvn -Dorg.nd4j.strumpf.resource.dirs=${GITHUB_WORKSPACE}/resources -Dorg.nd4j.test.resources.cache.dir=${GITHUB_WORKSPACE}/cache -Dgroups="long-running-tests, large-resources, distributed-systems" -Ptestresources -Pnd4j-tests-cpu -Dtest.offheap.size=14g -Dtest.heap.size=6g -Dsurefire.parallel.forcedTimeout=500 -Dsurefire.parallel.timeout=500 -Dsurefire.timeout=200 -Dsurefire.exitTimeout=500 test --fail-never -rf :nd4j

2
.github/workflows/run-cpu-tests-sanity-checks.yml vendored
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@ -34,5 +34,5 @@ jobs:
cmake --version
protoc --version
export OMP_NUM_THREADS=1
mvn -DskipTestResourceEnforcement=true -Ptestresources -pl ":deeplearning4j-modelimport,:deeplearning4j-core,:nd4j-native,:samediff-import,:libnd4j" -DexcludedGroups="long-running-tests,large-resources" -Pnd4j-tests-cpu --also-make clean test --fail-never
mvn -DskipTestResourceEnforcement=true -Ptestresources -pl ":deeplearning4j-modelimport,:deeplearning4j-core,:nd4j-native,:samediff-import,:libnd4j" -Ptest-nd4j-native --also-make clean test

56
.github/workflows/run-gpu-integration-tests-self-hosted.yml vendored
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@ -1,56 +0,0 @@
on:
workflow_dispatch:
jobs:
# Wait for up to a minute for previous run to complete, abort if not done by then
pre-ci:
runs-on: self-hosted
timeout-minutes: 1
steps:
- name: 'Block Concurrent Executions'
uses: softprops/turnstyle@v1
with:
poll-interval-seconds: 10
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
linux-x86_64:
needs: pre-ci
runs-on: [self-hosted]
steps:
- uses: AutoModality/action-clean@v1
- name: Cancel Previous Runs
uses: styfle/cancel-workflow-action@0.8.0
with:
access_token: ${{ github.token }}
- uses: actions/checkout@v2
- uses: ./.github/actions/download-dl4j-test-resources-linux
- name: Run gpu tests
shell: bash
env:
DEBIAN_FRONTEND: noninteractive
run: |
export PATH="/opt/protobuf/bin:/usr/local/cuda-11/bin:$PATH"
nvcc --version
mvn --version
cmake --version
protoc --version
export OMP_NUM_THREADS=1
mkdir -p ${GITHUB_WORKSPACE}/resources
mkdir -p ${GITHUB_WORKSPACE}/cache
export CUDA_VISIBLE_DEVICES=0
echo "Running tests for cuda 11.0"
export PATH="/opt/protobuf/bin:/usr/local/cuda-11.2/bin:$PATH"
mvn -Djavacpp.platform=linux-x86_64 -Dlibnd4j.chip=cuda -pl ":nd4j-cuda-11.0,:deeplearning4j-cuda-11.0,:libnd4j" --also-make -Pcuda clean --batch-mode install -DskipTests
mvn -Djunit.jupiter.execution.parallel.enabled=false -Dtest.offheap.size=6g -Pcuda -Dlibnd4j.chip=cuda -Dorg.nd4j.strumpf.resource.dirs=${GITHUB_WORKSPACE}/resources -Dorg.nd4j.test.resources.cache.dir=${GITHUB_WORKSPACE}/cache -DexcludedGroups="long-running-tests, large-resources, distributed-systems" -DskipTestResourceEnforcement=true -Ptestresources -Pintegration-tests -Pnd4j-tests-cuda clean test --fail-never -rf :nd4j
#mvn -Pcuda -Dlibnd4j.chip=cuda -Dorg.nd4j.strumpf.resource.dirs=${GITHUB_WORKSPACE}/resources -Dorg.nd4j.test.resources.cache.dir=${GITHUB_WORKSPACE}/cache -Dgroups="long-running-tests, large-resources, distributed-systems" -Ptestresources -Pnd4j-tests-cuda -Dtest.offheap.size=14g -Dtest.heap.size=6g -Dsurefire.parallel.forcedTimeout=200 -Dsurefire.parallel.timeout=200 -Dsurefire.timeout=200 -Dsurefire.exitTimeout=200 test --fail-never -rf :nd4j
echo "Running tests for cuda 11.2"
${GITHUB_WORKSPACE}/change-cuda-versions.sh 11.2
echo "Changed cuda to 11.2"
export PATH="/opt/protobuf/bin:/usr/local/cuda-11.2/bin:$PATH"
echo "Updated path for 11.2"
echo "Installing jars for 11.2"
mvn -Djavacpp.platform=linux-x86_64 -Dlibnd4j.chip=cuda -pl ":nd4j-cuda-11.2,:deeplearning4j-cuda-11.2,:libnd4j" --also-make -Pcuda clean --batch-mode install -DskipTests
echo "Installed jars for 11.2, running smaller tests for cuda 11.2"
mvn -Djunit.jupiter.execution.parallel.enabled=false -Dtest.offheap.size=4g -Pcuda -Dlibnd4j.chip=cuda -Dlibnd4j.chip=cuda -Dorg.nd4j.strumpf.resource.dirs=${GITHUB_WORKSPACE}/resources -Dorg.nd4j.test.resources.cache.dir=${GITHUB_WORKSPACE}/cache -DexcludedGroups="long-running-tests, large-resources, distributed-systems" -DskipTestResourceEnforcement=true -Ptestresources -Pintegration-tests -Pnd4j-tests-cuda clean test --fail-never -rf :nd4j
#echo "Running larger for cuda 11.2"
#mvn -Pcuda -Dlibnd4j.chip=cuda -Dorg.nd4j.strumpf.resource.dirs=${GITHUB_WORKSPACE}/resources -Dorg.nd4j.test.resources.cache.dir=${GITHUB_WORKSPACE}/cache -Dgroups="long-running-tests, large-resources, distributed-systems" -Ptestresources -Pnd4j-tests-cuda -Dtest.offheap.size=14g -Dtest.heap.size=6g -Dsurefire.parallel.forcedTimeout=200 -Dsurefire.parallel.timeout=200 -Dsurefire.timeout=200 -Dsurefire.exitTimeout=200 test --fail-never -rf :nd4j

2
.github/workflows/run-gpu-tests-sanity-checks.yml vendored
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@ -35,5 +35,5 @@ jobs:
protoc --version
bash ./change-cuda-versions.sh 11.2
export OMP_NUM_THREADS=1
mvn -DskipTestResourceEnforcement=true -Ptestresources -pl ":deeplearning4j-modelimport,:deeplearning4j-core,:nd4j-cuda-11.2,:samediff-import,:libnd4j" -Dlibnd4j.helper=cudnn -Ptest-nd4j-cuda --also-make -Dlibnd4j.chip=cuda clean test --fail-never
mvn -DskipTestResourceEnforcement=true -Ptestresources -pl ":deeplearning4j-modelimport,:deeplearning4j-core,:nd4j-cuda-11.2,:samediff-import,:libnd4j" -Dlibnd4j.compute="5.0 5.2 5.3 6.0 8.0" -Ptest-nd4j-cuda --also-make -Dlibnd4j.chip=cuda clean test

7
.gitignore vendored
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@ -48,7 +48,6 @@ release.properties
*.iml
*.prefs
*.dylib
lib/
.vs/
.vscode/
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/resources/bin
@ -75,9 +74,11 @@ nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/nativebla
*.orig
#libnd4j cmake
libnd4j/cmake*
bruai4j-native-common/cmake*
#vim
*.swp
*.dll
*.dll
/bruai4j-native/bruai4j-native-common/blasbuild/
/bruai4j-native/bruai4j-native-common/build/

2
ADRs/0002-ONNX_Runtime.md
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@ -1,7 +1,7 @@
# Onnx runtime module
## Status
Implemented
Proposed
Proposed by: Adam Gibson (23-09-2020)

2
ADRs/0003-Import_IR.md
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@ -2,7 +2,7 @@
## Status
Implemented
Proposed
Proposed by: Adam Gibson (28-09-2020)

3
ADRs/0003-NdArray_Strides_ArmCompute.md
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@ -1,8 +1,9 @@

# Libnd4j NdArray padded buffers, strides for Arm_Compute Library wrapper
## Status
Implemented
PROPOSED
Proposed by: Abdelrauf (23/09/2020)

2
ADRs/0004-Mapping_IR.md
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@ -1,7 +1,7 @@
# Import IR
## Status
Implemented
Proposed
Proposed by: Adam Gibson (28-09-2020)

2
ADRs/0005-Interpreter.md
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@ -1,7 +1,7 @@
# Interpreter
## Status
Rejected
Proposed
Proposed by: Adam Gibson (28-09-2020)

77
ADRs/0006 - Test architecture.md
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@ -1,77 +0,0 @@
# Junit 5 tag usage
## Status
Proposed
Proposed by: Adam Gibson (21-03-2021)
Discussed with: N/A
## Context
DL4J was a junit 4 based code based for testing.
It's now based on junit 5's jupiter API, which has support for [Tags](https://junit.org/junit5/docs/5.0.1/api/org/junit/jupiter/api/Tag.html).
DL4j's code base has a number of different kinds of tests that fall in to several categories:
1. Long and flaky involving distributed systems (spark, parameter-server)
2. Code that requires large downloads, but runs quickly
3. Quick tests that test basic functionality
4. Comprehensive integration tests that test several parts of a code base
Due to the variety of behaviors across different tests, it's hard to tell what's actually needed
for running and validating whether changes work against such a complex test base.
Much of the time, most of the tests aren't related to a given change.
Often times, quick sanity checks are all that's needed in order to make sure a change works.
A common set of tags is used to filter which tests are needed to run when.
This allows us to retain complex integration tests and run them on a set schedule
to catch regressions while allowing a defined subset of tests to run for a quick feedback loop.
## Decision
A few kinds of tags exist:
1. Time based: long-time,short-time
2. Network based: has-download
3. Distributed systems: spark, multi-threaded
4. Functional cross-cutting concerns: multi module tests, similar functionality (excludes time based)
5. Platform specific tests that can vary on different hardware: cpu, gpu
6. JVM crash: (jvm-crash) Tests with native code can crash the JVM for tests. It's useful to be able to turn those off when debugging.: jvm-crash
7. RNG: (rng) for RNG related tests
8. Samediff:(samediff) samediff related tests
9. Training related functionality
10. long-running-tests: The longer running tests that take a longer execution time
11. large-resources: tests requiring a large amount of ram/cpu (>= 2g up to 16g)
New maven properties for maven surefire:
test.offheap.size: tunes off heap size for javacpp
test.heap.size: tunes heap size of test jvms
Auto tuning the number of CPU cores for tests relative to the number of CPUs present
## Consequences
### Advantages
* Ability to sort through and filter tests based on different running environments
* Ability to reason about test suites as a whole dynamically across modules
* Avoid the need to define test suites
* Ability to define groups of tags based in profiles
* Ability to dynamically filter tests from the maven command line
### Disadvantages
* Documentation and maintenance burden needing to know what tags do what
* Test maintenance for newcomers who may not know how to tag tests

51
CONTRIBUTING.md
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@ -1,51 +0,0 @@
# Contributing to Deeplearning4j
Thanks for your interest in DL4J. Our goal is to bring fast, open-source deep learning to all JVM-based communities.
## Getting Started
Deeplearning4j's [open issues are here](https://github.com/eclipse/deeplearning4j/issues). In time, we'll tag issues that would make a good first pull request for new contributors. An easy way to get started helping the project is to *file an issue*. You can do that on the Deeplearning4j issues page by clicking on the green button at the right. Issues can include bugs to fix, features to add, or documentation that looks outdated.
Note that you will need to [build dl4j from source](https://deeplearning4j.org/docs/latest/deeplearning4j-build-from-source)
For some tips on contributing to open source, this [post is helpful](https://smartbear.com/blog/test-and-monitor/14-ways-to-contribute-to-open-source-without-being/).
## Contributions
Deeplearning4j welcomes contributions from everyone.
Contributions to Deeplearning4j should be made in the form of GitHub pull requests. Each pull request will
be reviewed by a core contributor (someone with permission to land patches) and either landed in the
main tree or given feedback for changes that would be required.
## Pull Request Checklist
- Branch from the master branch and, if needed, rebase to the current master
branch before submitting your pull request. If it doesn't merge cleanly with
master you may be asked to rebase your changes.
- Commits should be as small as possible, while ensuring that each commit is
correct independently (i.e., each commit should compile and pass tests).
- Don't put submodule updates in your pull request unless they are to landed
commits.
- If your patch is not getting reviewed or you need a specific person to review
it, you can @-reply a reviewer asking for a review in the pull request or a
comment.
- Work-in-progress pull requests are welcome. Please prefix them with `[WIP]` to tell the continuous integration (CI) backend not to run tests/checks on them (until that tag is removed and another commit is pushed up).
- Add tests relevant to the fixed bug or new feature.
## Conduct & License
We follow the [Rust Code of Conduct](http://www.rust-lang.org/conduct.html).
All code in this repository is released under the Apache Software Foundation License, 2.0, and by contributing to this repository, you agree to release that contribution under that same license.
## Eclipse Contributor Agreement and Commit Signing
Please see the following page for details: [https://deeplearning4j.org/eclipse-contributors](https://deeplearning4j.org/eclipse-contributors)

29
Jenkinsfile vendored
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@ -1,29 +0,0 @@
/* ******************************************************************************
*
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* See the NOTICE file distributed with this work for additional
* information regarding copyright ownership.
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
#!groovy
/*
To redefine some job/run parameters,
please provide arguments to jenkinsBuilder step.
Example: jenkinsBuilder platforms: []
*/
jenkinsBuilder()

192
LICENSE
View File

@ -1,4 +1,5 @@
Apache License
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
@ -178,7 +179,7 @@ Apache License
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "{}"
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
@ -186,7 +187,7 @@ Apache License
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright {yyyy} {name of copyright owner}
Copyright [yyyy] [name of copyright owner]
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@ -198,187 +199,4 @@ Apache License
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
##########################
Keras code
Auto-generated documentation: https://github.com/deeplearning4j/deeplearning4j/blob/master/docs/doc_generator.py
COPYRIGHT
All contributions by François Chollet:
Copyright (c) 2015 - 2018, François Chollet.
All rights reserved.
All contributions by Google:
Copyright (c) 2015 - 2018, Google, Inc.
All rights reserved.
All contributions by Microsoft:
Copyright (c) 2017 - 2018, Microsoft, Inc.
All rights reserved.
All other contributions:
Copyright (c) 2015 - 2018, the respective contributors.
All rights reserved.
Each contributor holds copyright over their respective contributions.
The project versioning (Git) records all such contribution source information.
The MIT License (MIT)
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
##########################
OpenCSV Code
CSVParser: https://github.com/deeplearning4j/deeplearning4j/blob/master/datavec/datavec-api/src/main/java/org/datavec/api/records/reader/impl/csv/SerializableCSVParser.java
Apache 2.0 License
All contributions by Bytecode Pty Ltd.
Copyright 2005 Bytecode Pty Ltd.
All rights reserved.
##########################
Aeron Code
Modifed Code: nd4j/nd4j-serde/nd4j-aeron/src/main/java/org/nd4j/aeron/ipc/AeronUtil.java
Copyright 2014 - 2016 Real Logic Ltd. All rights reserved.
Apache License, Version 2.0
##########################
cnpy Code
Forked Code: libnd4j/include/cnpy/
The MIT License
Copyright (c) Carl Rogers, 2011
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
##########################
Protocol Buffers Code
Codebase: nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/protobuf/tf/google/protobuf/
Protocol Buffers - Google's data interchange format
Copyright 2008 Google Inc. All rights reserved.
https://developers.google.com/protocol-buffers/
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above
copyright notice, this list of conditions and the following disclaimer
in the documentation and/or other materials provided with the
distribution.
* Neither the name of Google Inc. nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
##########################
ONNX Code
Protocol Buffers: nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/protobuf/onnx/
Copyright (c) Facebook Inc. and Microsoft Corporation. All rights reserved.
Licensed under the MIT license.
##########################
TensorFlow Code
Protocol Buffers: nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/protobuf/tf/tensorflow/core/
Operations: libnd4j/include/ops/declarable/generic/parity_ops/
Copyright 2015-2017 The TensorFlow Authors. All rights reserved.
Apache License, Version 2.0
##########################
Ansj Code
Codebase: deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp-chinese/src/main/java/org/ansj/
Resources: deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp-chinese/src/main/resources/
Copyright 2011-2016 ansj_seg. All rights reserved.
Apache License, Version 2.0
##########################
Kuromoji Code
Codebase: deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp-japanese/src/main/java/com/atilika/kuromoji/
Copyright (c) 2010-2015 Atilika Inc. and contributors. All rights reserved.
Apache License, Version 2.0
limitations under the License.

23
NOTICE Normal file
View File

@ -0,0 +1,23 @@
Brutex Network Deeplearning4j
Copyright 2021 Brutex Network Contributors
This product includes software developed at
The Apache Software Foundation (http://www.apache.org/).
This product includes software developed by
* Brian Rosenberger. Copyright (C) 2021 Brian Rosenberger.
This product includes software developed at
* Eclipse Deeplearning4j (Apache 2.0). Copyright 2020-2021 Eclipse Deeplearning4j Contributors
This product includes software developed at
* Skymind Inc (Apache 2.0). Copyright (C) 2015-2018 Skymind Inc.
This product includes software developed at
* Konduit KK (Apache 2.0). Copyright (C) 2020.
This product includes software from the Tensorflow Project (Apache 2.0).
* Copyright (C) 2015-2018 Tensorflow Authors.
This product includes software from the Onnx Project project (Apache 2.0).
* Copyright (C) 2020 Onnx Contributors (https://github.com/onnx/onnx)

20
NOTICE.txt
View File

@ -1,20 +0,0 @@
Eclipse Deeplearning4j
Copyright 2021 Eclipse Deeplearning4j Contributors
This product includes software developed at
The Apache Software Foundation (http://www.apache.org/).
This product includes software developed at
* Skymind Inc (Apache 2.0). Copyright (C) 2015-2018 Skymind Inc .
This product includes software developed at
* Konduit KK (Apache 2.0). Copyright (C) 2020.
This product includes software from the Tensorflow Project (Apache 2.0).
* Copyright (C) 2015-2018 Tensorflow Authors.
# https://github.com/onnx/onnx
This product includes software from the Onnx Project project (Apache 2.0).
* Copyright (C) 2020 Onnx Contributors (https://github.com/onnx/onnx)

18
README.md
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@ -2,15 +2,13 @@
<img src="https://www.zeljkoobrenovic.com/tools/tech/images/eclipse_deeplearning4j.png">
</p>
[![Documentation](https://img.shields.io/badge/user-documentation-blue.svg)](https://deeplearning4j.konduit.ai/)
[![Get help at the community forum](https://img.shields.io/badge/Get%20Help-Community%20Forum-blue)](https://community.konduit.ai/)
[![javadoc](https://javadoc.io/badge2/org.deeplearning4j/deeplearning4j-nn/DL4J%20API%20Doc.svg)](https://javadoc.io/doc/org.deeplearning4j/deeplearning4j-nn)
[![javadoc](https://javadoc.io/badge2/org.nd4j/nd4j-api/ND4J%20API%20Doc.svg)](https://javadoc.io/doc/org.nd4j/nd4j-api)
[![Documentation](https://img.shields.io/badge/user-documentation-blue.svg)](https://deeplearning4j.org)
[![Get help at the community forum](https://img.shields.io/badge/Get%20Help-Community%20Forum-blue)](https://www.reddit.com/r/deeplearning4j/)
[![javadoc](https://javadoc.io/badge2/org.deeplearning4j/deeplearning4j-nn/DL4J%20API%20Doc.svg)](https://deeplearning4j.org/api/latest/)
[![License](https://img.shields.io/github/license/eclipse/deeplearning4j)](LICENSE)
![GitHub commit activity](https://img.shields.io/github/commit-activity/m/konduitai/deeplearning4j)
The **[Eclipse Deeplearning4J](https://deeplearning4j.konduit.ai/)** (DL4J) ecosystem is a set of projects intended to support all the needs of a JVM based deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
The **[Eclipse Deeplearning4J](https://deeplearning4j.org)** (DL4J) ecosystem is a set of projects intended to support all the needs of a JVM based deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
Because Deeplearning4J runs on the JVM you can use it with a wide variety of JVM based languages other than Java, like Scala, Kotlin, Clojure and many more.
@ -22,7 +20,7 @@ The DL4J stack comprises of:
- **Arbiter**: Library for hyperparameter search
- **LibND4J** : C++ library that underpins everything. For more information on how the JVM acceses native arrays and operations refer to [JavaCPP](https://github.com/bytedeco/javacpp)
All projects in the DL4J ecosystem support Windows, Linux and macOS. Hardware support includes CUDA GPUs (10.0, 10.1, 10.2 except OSX), x86 CPU (x86_64, avx2, avx512), ARM CPU (arm, arm64, armhf) and PowerPC (ppc64le).
All projects in the DL4J ecosystem support Windows, Linux and macOS. Hardware support includes CUDA GPUs (11.2, 10.0, 10.1, 10.2 except OSX), x86 CPU (x86_64, avx2, avx512), ARM CPU (arm, arm64, armhf) and PowerPC (ppc64le).
## Using Eclipse Deeplearning4J in your project
@ -112,9 +110,3 @@ An example of GPU "CC" or compute capability is 61 for Titan X Pascal.
## License
[Apache License 2.0](LICENSE)
## Commercial Support
Deeplearning4J is actively developed by the team at [Konduit K.K.](http://www.konduit.ai).
[If you need any commercial support feel free to reach out to us.](https://konduit.ai/konduit-open-source-support/)

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@ -0,0 +1,24 @@
branches:
only:
- master
notifications:
email: false
dist: trusty
sudo: false
cache:
directories:
- $HOME/.m2
language: java
jdk:
- openjdk8
matrix:
include:
- os: linux
env: OS=linux-x86_64 SCALA=2.10
install: true
script: bash ./ci/build-linux-x86_64.sh
- os: linux
env: OS=linux-x86_64 SCALA=2.11
install: true
script: bash ./ci/build-linux-x86_64.sh

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@ -0,0 +1,45 @@
# Arbiter
A tool dedicated to tuning (hyperparameter optimization) of machine learning models. Part of the DL4J Suite of Machine Learning / Deep Learning tools for the enterprise.
## Modules
Arbiter contains the following modules:
- arbiter-core: Defines the API and core functionality, and also contains functionality for the Arbiter UI
- arbiter-deeplearning4j: For hyperparameter optimization of DL4J models (MultiLayerNetwork and ComputationGraph networks)
## Hyperparameter Optimization Functionality
The open-source version of Arbiter currently defines two methods of hyperparameter optimization:
- Grid search
- Random search
For optimization of complex models such as neural networks (those with more than a few hyperparameters), random search is superior to grid search, though Bayesian hyperparameter optimization schemes
For a comparison of random and grid search methods, see [Random Search for Hyper-parameter Optimization (Bergstra and Bengio, 2012)](http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf).
### Core Concepts and Classes in Arbiter for Hyperparameter Optimization
In order to conduct hyperparameter optimization in Arbiter, it is necessary for the user to understand and define the following:
- **Parameter Space**: A ```ParameterSpace<P>``` specifies the type and allowable values of hyperparameters for a model configuration of type ```P```. For example, ```P``` could be a MultiLayerConfiguration for DL4J
- **Candidate Generator**: A ```CandidateGenerator<C>``` is used to generate candidate models configurations of some type ```C```. The following implementations are defined in arbiter-core:
- ```RandomSearchCandidateGenerator```
- ```GridSearchCandidateGenerator```
- **Score Function**: A ```ScoreFunction<M,D>``` is used to score a model of type ```M``` given data of type ```D```. For example, in DL4J a score function might be used to calculate the classification accuracy from a DataSetIterator
- A key concept here is that they score is a single numerical (double precision) value that we either want to minimize or maximize - this is the goal of hyperparameter optimization
- **Termination Conditions**: One or more ```TerminationCondition``` instances must be provided to the ```OptimizationConfiguration```. ```TerminationCondition``` instances are used to control when hyperparameter optimization should be stopped. Some built-in termination conditions:
- ```MaxCandidatesCondition```: Terminate if more than the specified number of candidate hyperparameter configurations have been executed
- ```MaxTimeCondition```: Terminate after a specified amount of time has elapsed since starting the optimization
- **Result Saver**: The ```ResultSaver<C,M,A>``` interface is used to specify how the results of each hyperparameter optimization run should be saved. For example, whether saving should be done to local disk, to a database, to HDFS, or simply stored in memory.
- Note that ```ResultSaver.saveModel``` method returns a ```ResultReference``` object, which provides a mechanism for re-loading both the model and score from wherever it may be saved.
- **Optimization Configuration**: An ```OptimizationConfiguration<C,M,D,A>``` ties together the above configuration options in a fluent (builder) pattern.
- **Candidate Executor**: The ```CandidateExecutor<C,M,D,A>``` interface provides a layer of abstraction between the configuration and execution of each instance of learning. Currently, the only option is the ```LocalCandidateExecutor```, which is used to execute learning on a single machine (in the current JVM). In principle, other execution methods (for example, on Spark or cloud computing machines) could be implemented.
- **Optimization Runner**: The ```OptimizationRunner``` uses an ```OptimizationConfiguration``` and a ```CandidateExecutor``` to actually run the optimization, and save the results.
### Optimization of DeepLearning4J Models
(This section: forthcoming)

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<!--~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~ Copyright (c) 2015-2018 Skymind, Inc.
~
~ This program and the accompanying materials are made available under the
~ terms of the Apache License, Version 2.0 which is available at
~ https://www.apache.org/licenses/LICENSE-2.0.
~
~ Unless required by applicable law or agreed to in writing, software
~ distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
~ WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
~ License for the specific language governing permissions and limitations
~ under the License.
~
~ SPDX-License-Identifier: Apache-2.0
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~-->
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<parent>
<artifactId>arbiter</artifactId>
<groupId>net.brutex.ai</groupId>
<version>1.0.0-SNAPSHOT</version>
</parent>
<modelVersion>4.0.0</modelVersion>
<artifactId>arbiter-core</artifactId>
<packaging>jar</packaging>
<name>arbiter-core</name>
<dependencies>
<dependency>
<groupId>net.brutex.ai</groupId>
<artifactId>nd4j-api</artifactId>
<version>${project.version}</version>
<exclusions>
<exclusion>
<groupId>com.google.code.findbugs</groupId>
<artifactId>*</artifactId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>com.google.guava</groupId>
<artifactId>guava</artifactId>
<version>${guava.jre.version}</version>
</dependency>
<dependency>
<groupId>org.apache.commons</groupId>
<artifactId>commons-lang3</artifactId>
<version>${commons.lang.version}</version>
</dependency>
<dependency>
<groupId>org.apache.commons</groupId>
<artifactId>commons-math3</artifactId>
<version>${commons.math.version}</version>
</dependency>
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-api</artifactId>
<version>${slf4j.version}</version>
</dependency>
<dependency>
<groupId>joda-time</groupId>
<artifactId>joda-time</artifactId>
<version>${jodatime.version}</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-annotations</artifactId>
<version>${jackson.version}</version>
</dependency>
<dependency>
<groupId>net.brutex.ai</groupId>
<artifactId>deeplearning4j-common-tests</artifactId>
<version>${project.version}</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.datatype</groupId>
<artifactId>jackson-datatype-joda</artifactId>
<version>${jackson.version}</version>
</dependency>
<dependency>
<groupId>net.brutex.ai</groupId>
<artifactId>nd4j-native</artifactId>
<version>${project.version}</version>
<scope>test</scope>
<classifier>windows-x86_64</classifier>
</dependency>
</dependencies>
</project>

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@ -0,0 +1,91 @@
<!--~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~ Copyright (c) 2015-2018 Skymind, Inc.
~
~ This program and the accompanying materials are made available under the
~ terms of the Apache License, Version 2.0 which is available at
~ https://www.apache.org/licenses/LICENSE-2.0.
~
~ Unless required by applicable law or agreed to in writing, software
~ distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
~ WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
~ License for the specific language governing permissions and limitations
~ under the License.
~
~ SPDX-License-Identifier: Apache-2.0
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~-->
<assembly>
<id>bin</id>
<!-- START SNIPPET: formats -->
<formats>
<format>tar.gz</format>
<!--
<format>tar.bz2</format>
<format>zip</format>
-->
</formats>
<!-- END SNIPPET: formats -->
<dependencySets>
<dependencySet>
<outputDirectory>lib</outputDirectory>
<includes>
<include>*:jar:*</include>
</includes>
<excludes>
<exclude>*:sources</exclude>
</excludes>
</dependencySet>
</dependencySets>
<!-- START SNIPPET: fileSets -->
<fileSets>
<fileSet>
<includes>
<include>readme.txt</include>
</includes>
</fileSet>
<fileSet>
<directory>src/main/resources/bin/</directory>
<outputDirectory>bin</outputDirectory>
<includes>
<include>arbiter</include>
</includes>
<lineEnding>unix</lineEnding>
<fileMode>0755</fileMode>
</fileSet>
<fileSet>
<directory>examples</directory>
<outputDirectory>examples</outputDirectory>
<!--
<lineEnding>unix</lineEnding>
https://stackoverflow.com/questions/2958282/stranges-files-in-my-assembly-since-switching-to-lineendingunix-lineending
-->
</fileSet>
<!--
<fileSet>
<directory>src/bin</directory>
<outputDirectory>bin</outputDirectory>
<includes>
<include>hello</include>
</includes>
<lineEnding>unix</lineEnding>
<fileMode>0755</fileMode>
</fileSet>
-->
<fileSet>
<directory>target</directory>
<outputDirectory>./</outputDirectory>
<includes>
<include>*.jar</include>
</includes>
</fileSet>
</fileSets>
<!-- END SNIPPET: fileSets -->
</assembly>

74
arbiter/arbiter-core/src/main/java/org/deeplearning4j/arbiter/optimize/api/AbstractParameterSpace.java Normal file
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@ -0,0 +1,74 @@
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.api;
import java.lang.reflect.Field;
import java.util.ArrayList;
import java.util.LinkedHashMap;
import java.util.List;
import java.util.Map;
/**
* Created by Alex on 23/07/2017.
*/
public abstract class AbstractParameterSpace<T> implements ParameterSpace<T> {
@Override
public Map<String, ParameterSpace> getNestedSpaces() {
Map<String, ParameterSpace> m = new LinkedHashMap<>();
//Need to manually build and walk the class heirarchy...
Class<?> currClass = this.getClass();
List<Class<?>> classHeirarchy = new ArrayList<>();
while (currClass != Object.class) {
classHeirarchy.add(currClass);
currClass = currClass.getSuperclass();
}
for (int i = classHeirarchy.size() - 1; i >= 0; i--) {
//Use reflection here to avoid a mass of boilerplate code...
Field[] allFields = classHeirarchy.get(i).getDeclaredFields();
for (Field f : allFields) {
String name = f.getName();
Class<?> fieldClass = f.getType();
boolean isParamSpacefield = ParameterSpace.class.isAssignableFrom(fieldClass);
if (!isParamSpacefield) {
continue;
}
f.setAccessible(true);
ParameterSpace<?> p;
try {
p = (ParameterSpace<?>) f.get(this);
} catch (IllegalAccessException e) {
throw new RuntimeException(e);
}
if (p != null) {
m.put(name, p);
}
}
}
return m;
}
}

57
arbiter/arbiter-core/src/main/java/org/deeplearning4j/arbiter/optimize/api/Candidate.java Normal file
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@ -0,0 +1,57 @@
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.api;
import lombok.AllArgsConstructor;
import lombok.Data;
import org.deeplearning4j.arbiter.optimize.generator.util.SerializedSupplier;
import org.nd4j.common.function.Supplier;
import java.io.Serializable;
import java.util.Map;
/**
* Candidate: a proposed hyperparameter configuration.
* Also includes a map for data parameters, to configure things like data preprocessing, etc.
*/
@Data
@AllArgsConstructor
public class Candidate<C> implements Serializable {
private Supplier<C> supplier;
private int index;
private double[] flatParameters;
private Map<String, Object> dataParameters;
private Exception exception;
public Candidate(C value, int index, double[] flatParameters, Map<String,Object> dataParameters, Exception e) {
this(new SerializedSupplier<C>(value), index, flatParameters, dataParameters, e);
}
public Candidate(C value, int index, double[] flatParameters) {
this(new SerializedSupplier<C>(value), index, flatParameters);
}
public Candidate(Supplier<C> value, int index, double[] flatParameters) {
this(value, index, flatParameters, null, null);
}
public C getValue(){
return supplier.get();
}
}

68
arbiter/arbiter-core/src/main/java/org/deeplearning4j/arbiter/optimize/api/CandidateGenerator.java Normal file
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@ -0,0 +1,68 @@
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.api;
import org.deeplearning4j.arbiter.optimize.generator.GridSearchCandidateGenerator;
import org.deeplearning4j.arbiter.optimize.generator.RandomSearchGenerator;
import com.fasterxml.jackson.annotation.JsonInclude;
import com.fasterxml.jackson.annotation.JsonSubTypes;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
/**
* A CandidateGenerator proposes candidates (i.e., hyperparameter configurations) for evaluation.
* This abstraction allows for different ways of generating the next configuration to test; for example,
* random search, grid search, Bayesian optimization methods, etc.
*
* @author Alex Black
*/
@JsonInclude(JsonInclude.Include.NON_NULL)
@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
public interface CandidateGenerator {
/**
* Is this candidate generator able to generate more candidates? This will always return true in some
* cases, but some search strategies have a limit (grid search, for example)
*/
boolean hasMoreCandidates();
/**
* Generate a candidate hyperparameter configuration
*/
Candidate getCandidate();
/**
* Report results for the candidate generator.
*
* @param result The results to report
*/
void reportResults(OptimizationResult result);
/**
* @return Get the parameter space for this candidate generator
*/
ParameterSpace<?> getParameterSpace();
/**
* @param rngSeed Set the random number generator seed for the candidate generator
*/
void setRngSeed(long rngSeed);
/**
* @return The type (class) of the generated candidates
*/
Class<?> getCandidateType();
}

60
arbiter/arbiter-core/src/main/java/org/deeplearning4j/arbiter/optimize/api/OptimizationResult.java Normal file
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@ -0,0 +1,60 @@
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.api;
import lombok.Data;
import org.deeplearning4j.arbiter.optimize.api.saving.ResultReference;
import org.deeplearning4j.arbiter.optimize.runner.CandidateInfo;
import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
import com.fasterxml.jackson.annotation.JsonProperty;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
import java.io.Serializable;
/**
* An optimization result represents the results of an optimization run, including the canditate configuration, the
* trained model, the score for that model, and index of the model
*
* @author Alex Black
*/
@Data
@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
@JsonIgnoreProperties({"resultReference"})
public class OptimizationResult implements Serializable {
@JsonProperty
private Candidate candidate;
@JsonProperty
private Double score;
@JsonProperty
private int index;
@JsonProperty
private Object modelSpecificResults;
@JsonProperty
private CandidateInfo candidateInfo;
private ResultReference resultReference;
public OptimizationResult(Candidate candidate, Double score, int index, Object modelSpecificResults,
CandidateInfo candidateInfo, ResultReference resultReference) {
this.candidate = candidate;
this.score = score;
this.index = index;
this.modelSpecificResults = modelSpecificResults;
this.candidateInfo = candidateInfo;
this.resultReference = resultReference;
}
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.api;
import com.fasterxml.jackson.annotation.JsonIgnore;
import com.fasterxml.jackson.annotation.JsonInclude;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
import java.util.List;
import java.util.Map;
/**
* ParameterSpace: defines the acceptable ranges of values a given parameter may take.
* Note that parameter spaces can be simple (like {@code ParameterSpace<Double>}) or complicated, including
* multiple nested ParameterSpaces
*
* @author Alex Black
*/
@JsonInclude(JsonInclude.Include.NON_NULL)
@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
public interface ParameterSpace<P> {
/**
* Generate a candidate given a set of values. These values are then mapped to a specific candidate, using some
* mapping function (such as the prior probability distribution)
*
* @param parameterValues A set of values, each in the range [0,1], of length {@link #numParameters()}
*/
P getValue(double[] parameterValues);
/**
* Get the total number of parameters (hyperparameters) to be optimized. This includes optional parameters from
* different parameter subpaces. (Thus, not every parameter may be used in every candidate)
*
* @return Number of hyperparameters to be optimized
*/
int numParameters();
/**
* Collect a list of parameters, recursively. Note that leaf parameters are parameters that do not have any
* nested parameter spaces
*/
List<ParameterSpace> collectLeaves();
/**
* Get a list of nested parameter spaces by name. Note that the returned parameter spaces may in turn have further
* nested parameter spaces. The map should be empty for leaf parameter spaces
*
* @return A map of nested parameter spaces
*/
Map<String, ParameterSpace> getNestedSpaces();
/**
* Is this ParameterSpace a leaf? (i.e., does it contain other ParameterSpaces internally?)
*/
@JsonIgnore
boolean isLeaf();
/**
* For leaf ParameterSpaces: set the indices of the leaf ParameterSpace.
* Expects input of length {@link #numParameters()}. Throws exception if {@link #isLeaf()} is false.
*
* @param indices Indices to set. Length should equal {@link #numParameters()}
*/
void setIndices(int... indices);
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.api;
import org.deeplearning4j.arbiter.optimize.api.data.DataProvider;
import org.deeplearning4j.arbiter.optimize.api.data.DataSource;
import org.deeplearning4j.arbiter.optimize.api.score.ScoreFunction;
import org.deeplearning4j.arbiter.optimize.runner.IOptimizationRunner;
import org.deeplearning4j.arbiter.optimize.runner.listener.StatusListener;
import java.util.List;
import java.util.Properties;
import java.util.concurrent.Callable;
/**
* The TaskCreator is used to take a candidate configuration, data provider and score function, and create something
* that can be executed as a Callable
*
* @author Alex Black
*/
public interface TaskCreator {
/**
* Generate a callable that can be executed to conduct the training of this model (given the model configuration)
*
* @param candidate Candidate (model) configuration to be trained
* @param dataProvider DataProvider, for the data
* @param scoreFunction Score function to be used to evaluate the model
* @param statusListeners Status listeners, that can be used for callbacks (to UI, for example)
* @return A callable that returns an OptimizationResult, once optimization is complete
*/
@Deprecated
Callable<OptimizationResult> create(Candidate candidate, DataProvider dataProvider, ScoreFunction scoreFunction,
List<StatusListener> statusListeners, IOptimizationRunner runner);
/**
* Generate a callable that can be executed to conduct the training of this model (given the model configuration)
*
* @param candidate Candidate (model) configuration to be trained
* @param dataSource Data source
* @param dataSourceProperties Properties (may be null) for the data source
* @param scoreFunction Score function to be used to evaluate the model
* @param statusListeners Status listeners, that can be used for callbacks (to UI, for example)
* @return A callable that returns an OptimizationResult, once optimization is complete
*/
Callable<OptimizationResult> create(Candidate candidate, Class<? extends DataSource> dataSource, Properties dataSourceProperties,
ScoreFunction scoreFunction, List<StatusListener> statusListeners, IOptimizationRunner runner);
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.api;
import java.util.HashMap;
import java.util.Map;
public class TaskCreatorProvider {
private static Map<Class<? extends ParameterSpace>, Class<? extends TaskCreator>> map = new HashMap<>();
public synchronized static TaskCreator defaultTaskCreatorFor(Class<? extends ParameterSpace> paramSpaceClass){
Class<? extends TaskCreator> c = map.get(paramSpaceClass);
try {
if(c == null){
return null;
}
return c.newInstance();
} catch (Exception e){
throw new RuntimeException("Could not create new instance of task creator class: " + c + " - missing no-arg constructor?", e);
}
}
public synchronized static void registerDefaultTaskCreatorClass(Class<? extends ParameterSpace> spaceClass,
Class<? extends TaskCreator> creatorClass){
map.put(spaceClass, creatorClass);
}
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.api.adapter;
import lombok.AllArgsConstructor;
import org.deeplearning4j.arbiter.optimize.api.ParameterSpace;
import java.util.Collections;
import java.util.List;
import java.util.Map;
/**
* An abstract class used for adapting one type into another. Subclasses of this need to merely implement 2 simple methods
*
* @param <F> Type to convert from
* @param <T> Type to convert to
* @author Alex Black
*/
@AllArgsConstructor
public abstract class ParameterSpaceAdapter<F, T> implements ParameterSpace<T> {
protected abstract T convertValue(F from);
protected abstract ParameterSpace<F> underlying();
protected abstract String underlyingName();
@Override
public T getValue(double[] parameterValues) {
return convertValue(underlying().getValue(parameterValues));
}
@Override
public int numParameters() {
return underlying().numParameters();
}
@Override
public List<ParameterSpace> collectLeaves() {
ParameterSpace p = underlying();
if(p.isLeaf()){
return Collections.singletonList(p);
}
return underlying().collectLeaves();
}
@Override
public Map<String, ParameterSpace> getNestedSpaces() {
return Collections.singletonMap(underlyingName(), (ParameterSpace)underlying());
}
@Override
public boolean isLeaf() {
return false; //Underlying may be a leaf, however
}
@Override
public void setIndices(int... indices) {
underlying().setIndices(indices);
}
@Override
public String toString() {
return underlying().toString();
}
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.api.data;
import com.fasterxml.jackson.annotation.JsonInclude;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
import java.io.Serializable;
import java.util.Map;
/**
* DataProvider interface abstracts out the providing of data
* @deprecated Use {@link DataSource}
*/
@JsonInclude(JsonInclude.Include.NON_NULL)
@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
@Deprecated
public interface DataProvider extends Serializable {
/**
* Get training data given some parameters for the data.
* Data parameters map is used to specify things like batch
* size data preprocessing
*
* @param dataParameters Parameters for data. May be null or empty for default data
* @return training data
*/
Object trainData(Map<String, Object> dataParameters);
/**
* Get training data given some parameters for the data. Data parameters map is used to specify things like batch
* size data preprocessing
*
* @param dataParameters Parameters for data. May be null or empty for default data
* @return training data
*/
Object testData(Map<String, Object> dataParameters);
Class<?> getDataType();
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.api.data;
import lombok.Data;
import org.nd4j.linalg.dataset.api.iterator.DataSetIteratorFactory;
import java.util.Map;
/**
* This is a {@link DataProvider} for
* an {@link DataSetIteratorFactory} which
* based on a key of {@link DataSetIteratorFactoryProvider#FACTORY_KEY}
* will create {@link org.nd4j.linalg.dataset.api.iterator.DataSetIterator}
* for use with arbiter.
*
* This {@link DataProvider} is mainly meant for use for command line driven
* applications.
*
* @author Adam Gibson
*/
@Data
public class DataSetIteratorFactoryProvider implements DataProvider {
public final static String FACTORY_KEY = "org.deeplearning4j.arbiter.data.data.factory";
/**
* Get training data given some parameters for the data.
* Data parameters map is used to specify things like batch
* size data preprocessing
*
* @param dataParameters Parameters for data. May be null or empty for default data
* @return training data
*/
@Override
public DataSetIteratorFactory trainData(Map<String, Object> dataParameters) {
return create(dataParameters);
}
/**
* Get training data given some parameters for the data. Data parameters map
* is used to specify things like batch
* size data preprocessing
*
* @param dataParameters Parameters for data. May be null or empty for default data
* @return training data
*/
@Override
public DataSetIteratorFactory testData(Map<String, Object> dataParameters) {
return create(dataParameters);
}
@Override
public Class<?> getDataType() {
return DataSetIteratorFactory.class;
}
private DataSetIteratorFactory create(Map<String, Object> dataParameters) {
if (dataParameters == null)
throw new IllegalArgumentException(
"Data parameters is null. Please specify a class name to create a dataset iterator.");
if (!dataParameters.containsKey(FACTORY_KEY))
throw new IllegalArgumentException(
"No data set iterator factory class found. Please specify a class name with key "
+ FACTORY_KEY);
String value = dataParameters.get(FACTORY_KEY).toString();
try {
Class<? extends DataSetIteratorFactory> clazz =
(Class<? extends DataSetIteratorFactory>) Class.forName(value);
return clazz.newInstance();
} catch (Exception e) {
throw new RuntimeException("Could not create DataSetIteratorFactory instance - missing no-arg constructor?", e);
}
}
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.api.data;
import java.io.Serializable;
import java.util.Properties;
/**
* DataSource: defines where the data should come from for training and testing.
* Note that implementations must have a no-argument contsructor
*
* @author Alex Black
*/
public interface DataSource extends Serializable {
/**
* Configure the current data source with the specified properties
* Note: These properties are fixed for the training instance, and are optionally provided by the user
* at the configuration stage.
* The properties could be anything - and are usually specific to each DataSource implementation.
* For example, values such as batch size could be set using these properties
* @param properties Properties to apply to the data source instance
*/
void configure(Properties properties);
/**
* Get test data to be used for the optimization. Usually a DataSetIterator or MultiDataSetIterator
*/
Object trainData();
/**
* Get test data to be used for the optimization. Usually a DataSetIterator or MultiDataSetIterator
*/
Object testData();
/**
* The type of data returned by {@link #trainData()} and {@link #testData()}.
* Usually DataSetIterator or MultiDataSetIterator
* @return Class of the objects returned by trainData and testData
*/
Class<?> getDataType();
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.api.evaluation;
import org.deeplearning4j.arbiter.optimize.api.data.DataProvider;
import java.io.Serializable;
import java.util.List;
/**
* ModelEvaluator: Used to conduct additional evaluation.
* For example, this may be classification performance on a test set or similar
*/
public interface ModelEvaluator extends Serializable {
Object evaluateModel(Object model, DataProvider dataProvider);
/**
* @return The model types supported by this class
*/
List<Class<?>> getSupportedModelTypes();
/**
* @return The datatypes supported by this class
*/
List<Class<?>> getSupportedDataTypes();
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.api.saving;
import lombok.AllArgsConstructor;
import lombok.NoArgsConstructor;
import org.deeplearning4j.arbiter.optimize.api.OptimizationResult;
import java.io.IOException;
import java.util.Collections;
import java.util.List;
/**
* A simple class to store optimization results in-memory.
* Not recommended for large (or a large number of) models.
*/
@NoArgsConstructor
public class InMemoryResultSaver implements ResultSaver {
@Override
public ResultReference saveModel(OptimizationResult result, Object modelResult) throws IOException {
return new InMemoryResult(result, modelResult);
}
@Override
public List<Class<?>> getSupportedCandidateTypes() {
return Collections.<Class<?>>singletonList(Object.class);
}
@Override
public List<Class<?>> getSupportedModelTypes() {
return Collections.<Class<?>>singletonList(Object.class);
}
@AllArgsConstructor
private static class InMemoryResult implements ResultReference {
private OptimizationResult result;
private Object modelResult;
@Override
public OptimizationResult getResult() throws IOException {
return result;
}
@Override
public Object getResultModel() throws IOException {
return modelResult;
}
}
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.api.saving;
import org.deeplearning4j.arbiter.optimize.api.OptimizationResult;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
import java.io.IOException;
/**
* Idea: We can't store all results in memory in general (might have thousands of candidates with millions of
* parameters each)
* So instead: return a reference to the saved result. Idea is that the result may be saved to disk or a database,
* and we can easily load it back into memory (if/when required) using the getResult() method
*/
@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
public interface ResultReference {
OptimizationResult getResult() throws IOException;
Object getResultModel() throws IOException;
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.api.saving;
import org.deeplearning4j.arbiter.optimize.api.OptimizationResult;
import com.fasterxml.jackson.annotation.JsonInclude;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
import java.io.IOException;
import java.util.List;
/**
* The ResultSaver interface provides a means of saving models in such a way that they can be loaded back into memory later,
* regardless of where/how they are saved.
*
* @author Alex Black
*/
@JsonInclude(JsonInclude.Include.NON_NULL)
@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
public interface ResultSaver {
/**
* Save the model (including configuration and any additional evaluation/results)
*
* @param result Optimization result for the model to save
* @param modelResult Model result to save
* @return ResultReference, such that the result can be loaded back into memory
* @throws IOException If IO error occurs during model saving
*/
ResultReference saveModel(OptimizationResult result, Object modelResult) throws IOException;
/**
* @return The candidate types supported by this class
*/
List<Class<?>> getSupportedCandidateTypes();
/**
* @return The model types supported by this class
*/
List<Class<?>> getSupportedModelTypes();
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.api.score;
import org.deeplearning4j.arbiter.optimize.api.data.DataProvider;
import org.deeplearning4j.arbiter.optimize.api.data.DataSource;
import com.fasterxml.jackson.annotation.JsonInclude;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
import java.io.Serializable;
import java.util.List;
import java.util.Map;
import java.util.Properties;
/**
* ScoreFunction defines the objective of hyperparameter optimization.
* Specifically, it is used to calculate a score for a given model, relative to the data set provided
* in the configuration.
*
*/
@JsonInclude(JsonInclude.Include.NON_NULL)
@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
public interface ScoreFunction extends Serializable {
/**
* Calculate and return the score, for the given model and data provider
*
* @param model Model to score
* @param dataProvider Data provider - data to use
* @param dataParameters Parameters for data
* @return Calculated score
*/
double score(Object model, DataProvider dataProvider, Map<String, Object> dataParameters);
/**
* Calculate and return the score, for the given model and data provider
*
* @param model Model to score
* @param dataSource Data source
* @param dataSourceProperties data source properties
* @return Calculated score
*/
double score(Object model, Class<? extends DataSource> dataSource, Properties dataSourceProperties);
/**
* Should this score function be minimized or maximized?
*
* @return true if score should be minimized, false if score should be maximized
*/
boolean minimize();
/**
* @return The model types supported by this class
*/
List<Class<?>> getSupportedModelTypes();
/**
* @return The data types supported by this class
*/
List<Class<?>> getSupportedDataTypes();
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.api.termination;
import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;
import org.deeplearning4j.arbiter.optimize.runner.IOptimizationRunner;
import com.fasterxml.jackson.annotation.JsonProperty;
/**
* Terminate hyperparameter search when the number of candidates exceeds a specified value.
* Note that this is counted as number of completed candidates, plus number of failed candidates.
*/
@AllArgsConstructor
@NoArgsConstructor
@Data
public class MaxCandidatesCondition implements TerminationCondition {
@JsonProperty
private int maxCandidates;
@Override
public void initialize(IOptimizationRunner optimizationRunner) {
//No op
}
@Override
public boolean terminate(IOptimizationRunner optimizationRunner) {
return optimizationRunner.numCandidatesCompleted() + optimizationRunner.numCandidatesFailed() >= maxCandidates;
}
@Override
public String toString() {
return "MaxCandidatesCondition(" + maxCandidates + ")";
}
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.api.termination;
import lombok.Data;
import lombok.NoArgsConstructor;
import org.deeplearning4j.arbiter.optimize.runner.IOptimizationRunner;
import org.joda.time.format.DateTimeFormat;
import org.joda.time.format.DateTimeFormatter;
import com.fasterxml.jackson.annotation.JsonProperty;
import java.util.concurrent.TimeUnit;
/**
* Terminate hyperparameter optimization after
* a fixed amount of time has passed
* @author Alex Black
*/
@NoArgsConstructor
@Data
public class MaxTimeCondition implements TerminationCondition {
private static final DateTimeFormatter formatter = DateTimeFormat.forPattern("dd-MMM HH:mm ZZ");
private long duration;
private TimeUnit timeUnit;
private long startTime;
private long endTime;
private MaxTimeCondition(@JsonProperty("duration") long duration, @JsonProperty("timeUnit") TimeUnit timeUnit,
@JsonProperty("startTime") long startTime, @JsonProperty("endTime") long endTime) {
this.duration = duration;
this.timeUnit = timeUnit;
this.startTime = startTime;
this.endTime = endTime;
}
/**
* @param duration Duration of time
* @param timeUnit Unit that the duration is specified in
*/
public MaxTimeCondition(long duration, TimeUnit timeUnit) {
this.duration = duration;
this.timeUnit = timeUnit;
}
@Override
public void initialize(IOptimizationRunner optimizationRunner) {
startTime = System.currentTimeMillis();
this.endTime = startTime + timeUnit.toMillis(duration);
}
@Override
public boolean terminate(IOptimizationRunner optimizationRunner) {
return System.currentTimeMillis() >= endTime;
}
@Override
public String toString() {
if (startTime > 0) {
return "MaxTimeCondition(" + duration + "," + timeUnit + ",start=\"" + formatter.print(startTime)
+ "\",end=\"" + formatter.print(endTime) + "\")";
} else {
return "MaxTimeCondition(" + duration + "," + timeUnit + "\")";
}
}
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.api.termination;
import org.deeplearning4j.arbiter.optimize.runner.IOptimizationRunner;
import com.fasterxml.jackson.annotation.JsonInclude;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
/**
* Global termination condition for conducting hyperparameter optimization.
* Termination conditions are used to determine if/when the optimization should stop.
*/
@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
@JsonInclude(JsonInclude.Include.NON_NULL)
public interface TerminationCondition {
/**
* Initialize the termination condition (such as starting timers, etc).
*/
void initialize(IOptimizationRunner optimizationRunner);
/**
* Determine whether optimization should be terminated
*
* @param optimizationRunner Optimization runner
* @return true if learning should be terminated, false otherwise
*/
boolean terminate(IOptimizationRunner optimizationRunner);
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.config;
import lombok.*;
import org.deeplearning4j.arbiter.optimize.api.CandidateGenerator;
import org.deeplearning4j.arbiter.optimize.api.data.DataProvider;
import org.deeplearning4j.arbiter.optimize.api.data.DataSource;
import org.deeplearning4j.arbiter.optimize.api.saving.ResultSaver;
import org.deeplearning4j.arbiter.optimize.api.score.ScoreFunction;
import org.deeplearning4j.arbiter.optimize.api.termination.TerminationCondition;
import org.deeplearning4j.arbiter.optimize.serde.jackson.JsonMapper;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
import com.fasterxml.jackson.core.JsonProcessingException;
import com.fasterxml.jackson.databind.annotation.JsonSerialize;
import java.io.IOException;
import java.lang.reflect.Constructor;
import java.util.Arrays;
import java.util.List;
import java.util.Properties;
/**
* OptimizationConfiguration ties together all of the various
* components (such as data, score functions, result saving etc)
* required to execute hyperparameter optimization.
*
* @author Alex Black
*/
@Data
@NoArgsConstructor
@EqualsAndHashCode(exclude = {"dataProvider", "terminationConditions", "candidateGenerator", "resultSaver"})
@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
public class OptimizationConfiguration {
@JsonSerialize
private DataProvider dataProvider;
@JsonSerialize
private Class<? extends DataSource> dataSource;
@JsonSerialize
private Properties dataSourceProperties;
@JsonSerialize
private CandidateGenerator candidateGenerator;
@JsonSerialize
private ResultSaver resultSaver;
@JsonSerialize
private ScoreFunction scoreFunction;
@JsonSerialize
private List<TerminationCondition> terminationConditions;
@JsonSerialize
private Long rngSeed;
@Getter
@Setter
private long executionStartTime;
private OptimizationConfiguration(Builder builder) {
this.dataProvider = builder.dataProvider;
this.dataSource = builder.dataSource;
this.dataSourceProperties = builder.dataSourceProperties;
this.candidateGenerator = builder.candidateGenerator;
this.resultSaver = builder.resultSaver;
this.scoreFunction = builder.scoreFunction;
this.terminationConditions = builder.terminationConditions;
this.rngSeed = builder.rngSeed;
if (rngSeed != null)
candidateGenerator.setRngSeed(rngSeed);
//Validate the configuration: data types, score types, etc
//TODO
//Validate that the dataSource has a no-arg constructor
if (dataSource != null) {
try {
dataSource.getConstructor();
} catch (NoSuchMethodException e) {
throw new IllegalStateException("Data source class " + dataSource.getName() + " does not have a public no-argument constructor");
}
}
}
public static class Builder {
private DataProvider dataProvider;
private Class<? extends DataSource> dataSource;
private Properties dataSourceProperties;
private CandidateGenerator candidateGenerator;
private ResultSaver resultSaver;
private ScoreFunction scoreFunction;
private List<TerminationCondition> terminationConditions;
private Long rngSeed;
/**
* @deprecated Use {@link #dataSource(Class, Properties)}
*/
@Deprecated
public Builder dataProvider(DataProvider dataProvider) {
this.dataProvider = dataProvider;
return this;
}
/**
* DataSource: defines where the data should come from for training and testing.
* Note that implementations must have a no-argument contsructor
*
* @param dataSource Class for the data source
* @param dataSourceProperties May be null. Properties for configuring the data source
*/
public Builder dataSource(Class<? extends DataSource> dataSource, Properties dataSourceProperties) {
this.dataSource = dataSource;
this.dataSourceProperties = dataSourceProperties;
return this;
}
public Builder candidateGenerator(CandidateGenerator candidateGenerator) {
this.candidateGenerator = candidateGenerator;
return this;
}
public Builder modelSaver(ResultSaver resultSaver) {
this.resultSaver = resultSaver;
return this;
}
public Builder scoreFunction(ScoreFunction scoreFunction) {
this.scoreFunction = scoreFunction;
return this;
}
/**
* Termination conditions to use
*
* @param conditions
* @return
*/
public Builder terminationConditions(TerminationCondition... conditions) {
terminationConditions = Arrays.asList(conditions);
return this;
}
public Builder terminationConditions(List<TerminationCondition> terminationConditions) {
this.terminationConditions = terminationConditions;
return this;
}
public Builder rngSeed(long rngSeed) {
this.rngSeed = rngSeed;
return this;
}
public OptimizationConfiguration build() {
return new OptimizationConfiguration(this);
}
}
/**
* Create an optimization configuration from the json
*
* @param json the json to create the config from
* For type definitions
* @see OptimizationConfiguration
*/
public static OptimizationConfiguration fromYaml(String json) {
try {
return JsonMapper.getYamlMapper().readValue(json, OptimizationConfiguration.class);
} catch (IOException e) {
throw new RuntimeException(e);
}
}
/**
* Create an optimization configuration from the json
*
* @param json the json to create the config from
* @see OptimizationConfiguration
*/
public static OptimizationConfiguration fromJson(String json) {
try {
return JsonMapper.getMapper().readValue(json, OptimizationConfiguration.class);
} catch (IOException e) {
throw new RuntimeException(e);
}
}
/**
* Return a json configuration of this optimization configuration
*
* @return
*/
public String toJson() {
try {
return JsonMapper.getMapper().writeValueAsString(this);
} catch (JsonProcessingException e) {
throw new RuntimeException(e);
}
}
/**
* Return a yaml configuration of this optimization configuration
*
* @return
*/
public String toYaml() {
try {
return JsonMapper.getYamlMapper().writeValueAsString(this);
} catch (JsonProcessingException e) {
throw new RuntimeException(e);
}
}
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.distribution;
import org.apache.commons.math3.distribution.IntegerDistribution;
import org.apache.commons.math3.exception.NumberIsTooLargeException;
import org.apache.commons.math3.exception.OutOfRangeException;
/**
* Degenerate distribution: i.e., integer "distribution" that is just a fixed value
*/
public class DegenerateIntegerDistribution implements IntegerDistribution {
private int value;
public DegenerateIntegerDistribution(int value) {
this.value = value;
}
@Override
public double probability(int x) {
return (x == value ? 1.0 : 0.0);
}
@Override
public double cumulativeProbability(int x) {
return (x >= value ? 1.0 : 0.0);
}
@Override
public double cumulativeProbability(int x0, int x1) throws NumberIsTooLargeException {
return (value >= x0 && value <= x1 ? 1.0 : 0.0);
}
@Override
public int inverseCumulativeProbability(double p) throws OutOfRangeException {
throw new UnsupportedOperationException();
}
@Override
public double getNumericalMean() {
return value;
}
@Override
public double getNumericalVariance() {
return 0;
}
@Override
public int getSupportLowerBound() {
return value;
}
@Override
public int getSupportUpperBound() {
return value;
}
@Override
public boolean isSupportConnected() {
return true;
}
@Override
public void reseedRandomGenerator(long seed) {
//no op
}
@Override
public int sample() {
return value;
}
@Override
public int[] sample(int sampleSize) {
int[] out = new int[sampleSize];
for (int i = 0; i < out.length; i++)
out[i] = value;
return out;
}
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.distribution;
import org.apache.commons.math3.distribution.*;
/**
* Distribution utils for Apache Commons math distributions - which don't provide equals, hashcode, toString methods,
* don't implement serializable etc.
* Which makes unit testing etc quite difficult.
*
* @author Alex Black
*/
public class DistributionUtils {
private DistributionUtils() {}
public static boolean distributionsEqual(RealDistribution a, RealDistribution b) {
if (a.getClass() != b.getClass())
return false;
Class<?> c = a.getClass();
if (c == BetaDistribution.class) {
BetaDistribution ba = (BetaDistribution) a;
BetaDistribution bb = (BetaDistribution) b;
return ba.getAlpha() == bb.getAlpha() && ba.getBeta() == bb.getBeta();
} else if (c == CauchyDistribution.class) {
CauchyDistribution ca = (CauchyDistribution) a;
CauchyDistribution cb = (CauchyDistribution) b;
return ca.getMedian() == cb.getMedian() && ca.getScale() == cb.getScale();
} else if (c == ChiSquaredDistribution.class) {
ChiSquaredDistribution ca = (ChiSquaredDistribution) a;
ChiSquaredDistribution cb = (ChiSquaredDistribution) b;
return ca.getDegreesOfFreedom() == cb.getDegreesOfFreedom();
} else if (c == ExponentialDistribution.class) {
ExponentialDistribution ea = (ExponentialDistribution) a;
ExponentialDistribution eb = (ExponentialDistribution) b;
return ea.getMean() == eb.getMean();
} else if (c == FDistribution.class) {
FDistribution fa = (FDistribution) a;
FDistribution fb = (FDistribution) b;
return fa.getNumeratorDegreesOfFreedom() == fb.getNumeratorDegreesOfFreedom()
&& fa.getDenominatorDegreesOfFreedom() == fb.getDenominatorDegreesOfFreedom();
} else if (c == GammaDistribution.class) {
GammaDistribution ga = (GammaDistribution) a;
GammaDistribution gb = (GammaDistribution) b;
return ga.getShape() == gb.getShape() && ga.getScale() == gb.getScale();
} else if (c == LevyDistribution.class) {
LevyDistribution la = (LevyDistribution) a;
LevyDistribution lb = (LevyDistribution) b;
return la.getLocation() == lb.getLocation() && la.getScale() == lb.getScale();
} else if (c == LogNormalDistribution.class) {
LogNormalDistribution la = (LogNormalDistribution) a;
LogNormalDistribution lb = (LogNormalDistribution) b;
return la.getScale() == lb.getScale() && la.getShape() == lb.getShape();
} else if (c == NormalDistribution.class) {
NormalDistribution na = (NormalDistribution) a;
NormalDistribution nb = (NormalDistribution) b;
return na.getMean() == nb.getMean() && na.getStandardDeviation() == nb.getStandardDeviation();
} else if (c == ParetoDistribution.class) {
ParetoDistribution pa = (ParetoDistribution) a;
ParetoDistribution pb = (ParetoDistribution) b;
return pa.getScale() == pb.getScale() && pa.getShape() == pb.getShape();
} else if (c == TDistribution.class) {
TDistribution ta = (TDistribution) a;
TDistribution tb = (TDistribution) b;
return ta.getDegreesOfFreedom() == tb.getDegreesOfFreedom();
} else if (c == TriangularDistribution.class) {
TriangularDistribution ta = (TriangularDistribution) a;
TriangularDistribution tb = (TriangularDistribution) b;
return ta.getSupportLowerBound() == tb.getSupportLowerBound()
&& ta.getSupportUpperBound() == tb.getSupportUpperBound() && ta.getMode() == tb.getMode();
} else if (c == UniformRealDistribution.class) {
UniformRealDistribution ua = (UniformRealDistribution) a;
UniformRealDistribution ub = (UniformRealDistribution) b;
return ua.getSupportLowerBound() == ub.getSupportLowerBound()
&& ua.getSupportUpperBound() == ub.getSupportUpperBound();
} else if (c == WeibullDistribution.class) {
WeibullDistribution wa = (WeibullDistribution) a;
WeibullDistribution wb = (WeibullDistribution) b;
return wa.getShape() == wb.getShape() && wa.getScale() == wb.getScale();
} else if (c == LogUniformDistribution.class ){
LogUniformDistribution lu_a = (LogUniformDistribution)a;
LogUniformDistribution lu_b = (LogUniformDistribution)b;
return lu_a.getMin() == lu_b.getMin() && lu_a.getMax() == lu_b.getMax();
} else {
throw new UnsupportedOperationException("Unknown or not supported RealDistribution: " + c);
}
}
public static boolean distributionEquals(IntegerDistribution a, IntegerDistribution b) {
if (a.getClass() != b.getClass())
return false;
Class<?> c = a.getClass();
if (c == BinomialDistribution.class) {
BinomialDistribution ba = (BinomialDistribution) a;
BinomialDistribution bb = (BinomialDistribution) b;
return ba.getNumberOfTrials() == bb.getNumberOfTrials()
&& ba.getProbabilityOfSuccess() == bb.getProbabilityOfSuccess();
} else if (c == GeometricDistribution.class) {
GeometricDistribution ga = (GeometricDistribution) a;
GeometricDistribution gb = (GeometricDistribution) b;
return ga.getProbabilityOfSuccess() == gb.getProbabilityOfSuccess();
} else if (c == HypergeometricDistribution.class) {
HypergeometricDistribution ha = (HypergeometricDistribution) a;
HypergeometricDistribution hb = (HypergeometricDistribution) b;
return ha.getPopulationSize() == hb.getPopulationSize()
&& ha.getNumberOfSuccesses() == hb.getNumberOfSuccesses()
&& ha.getSampleSize() == hb.getSampleSize();
} else if (c == PascalDistribution.class) {
PascalDistribution pa = (PascalDistribution) a;
PascalDistribution pb = (PascalDistribution) b;
return pa.getNumberOfSuccesses() == pb.getNumberOfSuccesses()
&& pa.getProbabilityOfSuccess() == pb.getProbabilityOfSuccess();
} else if (c == PoissonDistribution.class) {
PoissonDistribution pa = (PoissonDistribution) a;
PoissonDistribution pb = (PoissonDistribution) b;
return pa.getMean() == pb.getMean();
} else if (c == UniformIntegerDistribution.class) {
UniformIntegerDistribution ua = (UniformIntegerDistribution) a;
UniformIntegerDistribution ub = (UniformIntegerDistribution) b;
return ua.getSupportUpperBound() == ub.getSupportUpperBound()
&& ua.getSupportUpperBound() == ub.getSupportUpperBound();
} else if (c == ZipfDistribution.class) {
ZipfDistribution za = (ZipfDistribution) a;
ZipfDistribution zb = (ZipfDistribution) b;
return za.getNumberOfElements() == zb.getNumberOfElements() && za.getExponent() == zb.getNumberOfElements();
} else {
throw new UnsupportedOperationException("Unknown or not supported IntegerDistribution: " + c);
}
}
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.distribution;
import com.google.common.base.Preconditions;
import lombok.Getter;
import org.apache.commons.math3.distribution.RealDistribution;
import org.apache.commons.math3.exception.NumberIsTooLargeException;
import org.apache.commons.math3.exception.OutOfRangeException;
import java.util.Random;
/**
* Log uniform distribution, with support in range [min, max] for min &gt; 0
*
* Reference: <a href="https://www.vosesoftware.com/riskwiki/LogUniformdistribution.php">https://www.vosesoftware.com/riskwiki/LogUniformdistribution.php</a>
*
* @author Alex Black
*/
public class LogUniformDistribution implements RealDistribution {
@Getter private final double min;
@Getter private final double max;
private final double logMin;
private final double logMax;
private transient Random rng = new Random();
/**
*
* @param min Minimum value
* @param max Maximum value
*/
public LogUniformDistribution(double min, double max) {
Preconditions.checkArgument(min > 0, "Minimum must be > 0. Got: " + min);
Preconditions.checkArgument(max > min, "Maximum must be > min. Got: (min, max)=("
+ min + "," + max + ")");
this.min = min;
this.max = max;
this.logMin = Math.log(min);
this.logMax = Math.log(max);
}
@Override
public double probability(double x) {
if(x < min || x > max){
return 0;
}
return 1.0 / (x * (logMax - logMin));
}
@Override
public double density(double x) {
return probability(x);
}
@Override
public double cumulativeProbability(double x) {
if(x <= min){
return 0.0;
} else if(x >= max){
return 1.0;
}
return (Math.log(x)-logMin)/(logMax-logMin);
}
@Override
public double cumulativeProbability(double x0, double x1) throws NumberIsTooLargeException {
return cumulativeProbability(x1) - cumulativeProbability(x0);
}
@Override
public double inverseCumulativeProbability(double p) throws OutOfRangeException {
Preconditions.checkArgument(p >= 0 && p <= 1, "Invalid input: " + p);
return Math.exp(p * (logMax-logMin) + logMin);
}
@Override
public double getNumericalMean() {
return (max-min)/(logMax-logMin);
}
@Override
public double getNumericalVariance() {
double d1 = (logMax-logMin)*(max*max - min*min) - 2*(max-min)*(max-min);
return d1 / (2*Math.pow(logMax-logMin, 2.0));
}
@Override
public double getSupportLowerBound() {
return min;
}
@Override
public double getSupportUpperBound() {
return max;
}
@Override
public boolean isSupportLowerBoundInclusive() {
return true;
}
@Override
public boolean isSupportUpperBoundInclusive() {
return true;
}
@Override
public boolean isSupportConnected() {
return true;
}
@Override
public void reseedRandomGenerator(long seed) {
rng.setSeed(seed);
}
@Override
public double sample() {
return inverseCumulativeProbability(rng.nextDouble());
}
@Override
public double[] sample(int sampleSize) {
double[] d = new double[sampleSize];
for( int i=0; i<sampleSize; i++ ){
d[i] = sample();
}
return d;
}
@Override
public String toString(){
return "LogUniformDistribution(min=" + min + ",max=" + max + ")";
}
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator;
import lombok.Data;
import lombok.EqualsAndHashCode;
import org.apache.commons.math3.random.JDKRandomGenerator;
import org.apache.commons.math3.random.SynchronizedRandomGenerator;
import org.deeplearning4j.arbiter.optimize.api.CandidateGenerator;
import org.deeplearning4j.arbiter.optimize.api.OptimizationResult;
import org.deeplearning4j.arbiter.optimize.api.ParameterSpace;
import org.deeplearning4j.arbiter.util.LeafUtils;
import java.util.List;
import java.util.Map;
import java.util.concurrent.atomic.AtomicInteger;
/**
* BaseCandidateGenerator: abstract class upon which {@link RandomSearchGenerator},
* {@link GridSearchCandidateGenerator} and {@link GeneticSearchCandidateGenerator}
* are built.
*
* @param <T> Type of candidates to generate
*/
@Data
@EqualsAndHashCode(exclude = {"rng", "candidateCounter"})
public abstract class BaseCandidateGenerator<T> implements CandidateGenerator {
protected ParameterSpace<T> parameterSpace;
protected AtomicInteger candidateCounter = new AtomicInteger(0);
protected SynchronizedRandomGenerator rng = new SynchronizedRandomGenerator(new JDKRandomGenerator());
protected Map<String, Object> dataParameters;
protected boolean initDone = false;
public BaseCandidateGenerator(ParameterSpace<T> parameterSpace, Map<String, Object> dataParameters,
boolean initDone) {
this.parameterSpace = parameterSpace;
this.dataParameters = dataParameters;
this.initDone = initDone;
}
protected void initialize() {
if(!initDone) {
//First: collect leaf parameter spaces objects and remove duplicates
List<ParameterSpace> noDuplicatesList = LeafUtils.getUniqueObjects(parameterSpace.collectLeaves());
//Second: assign each a number
int i = 0;
for (ParameterSpace ps : noDuplicatesList) {
int np = ps.numParameters();
if (np == 1) {
ps.setIndices(i++);
} else {
int[] values = new int[np];
for (int j = 0; j < np; j++)
values[j] = i++;
ps.setIndices(values);
}
}
initDone = true;
}
}
@Override
public ParameterSpace<T> getParameterSpace() {
return parameterSpace;
}
@Override
public void reportResults(OptimizationResult result) {
//No op
}
@Override
public void setRngSeed(long rngSeed) {
rng.setSeed(rngSeed);
}
}

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/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator;
import lombok.Getter;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.arbiter.optimize.api.Candidate;
import org.deeplearning4j.arbiter.optimize.api.OptimizationResult;
import org.deeplearning4j.arbiter.optimize.api.ParameterSpace;
import org.deeplearning4j.arbiter.optimize.api.score.ScoreFunction;
import org.deeplearning4j.arbiter.optimize.generator.genetic.Chromosome;
import org.deeplearning4j.arbiter.optimize.generator.genetic.ChromosomeFactory;
import org.deeplearning4j.arbiter.optimize.generator.genetic.exceptions.GeneticGenerationException;
import org.deeplearning4j.arbiter.optimize.generator.genetic.population.EmptyPopulationInitializer;
import org.deeplearning4j.arbiter.optimize.generator.genetic.population.PopulationInitializer;
import org.deeplearning4j.arbiter.optimize.generator.genetic.population.PopulationModel;
import org.deeplearning4j.arbiter.optimize.generator.genetic.selection.GeneticSelectionOperator;
import org.deeplearning4j.arbiter.optimize.generator.genetic.selection.SelectionOperator;
import java.util.Map;
/**
* Uses a genetic algorithm to generate candidates.
*
* @author Alexandre Boulanger
*/
@Slf4j
public class GeneticSearchCandidateGenerator extends BaseCandidateGenerator {
@Getter
protected final PopulationModel populationModel;
protected final ChromosomeFactory chromosomeFactory;
protected final SelectionOperator selectionOperator;
protected boolean hasMoreCandidates = true;
public static class Builder {
protected final ParameterSpace<?> parameterSpace;
protected Map<String, Object> dataParameters;
protected boolean initDone;
protected boolean minimizeScore;
protected PopulationModel populationModel;
protected ChromosomeFactory chromosomeFactory;
protected SelectionOperator selectionOperator;
/**
* @param parameterSpace ParameterSpace from which to generate candidates
* @param scoreFunction The score function that will be used in the OptimizationConfiguration
*/
public Builder(ParameterSpace<?> parameterSpace, ScoreFunction scoreFunction) {
this.parameterSpace = parameterSpace;
this.minimizeScore = scoreFunction.minimize();
}
/**
* @param populationModel The PopulationModel instance to use.
*/
public Builder populationModel(PopulationModel populationModel) {
this.populationModel = populationModel;
return this;
}
/**
* @param selectionOperator The SelectionOperator to use. Default is GeneticSelectionOperator
*/
public Builder selectionOperator(SelectionOperator selectionOperator) {
this.selectionOperator = selectionOperator;
return this;
}
public Builder dataParameters(Map<String, Object> dataParameters) {
this.dataParameters = dataParameters;
return this;
}
public GeneticSearchCandidateGenerator.Builder initDone(boolean initDone) {
this.initDone = initDone;
return this;
}
/**
* @param chromosomeFactory The ChromosomeFactory to use
*/
public Builder chromosomeFactory(ChromosomeFactory chromosomeFactory) {
this.chromosomeFactory = chromosomeFactory;
return this;
}
public GeneticSearchCandidateGenerator build() {
if (populationModel == null) {
PopulationInitializer defaultPopulationInitializer = new EmptyPopulationInitializer();
populationModel = new PopulationModel.Builder().populationInitializer(defaultPopulationInitializer)
.build();
}
if (chromosomeFactory == null) {
chromosomeFactory = new ChromosomeFactory();
}
if (selectionOperator == null) {
selectionOperator = new GeneticSelectionOperator.Builder().build();
}
return new GeneticSearchCandidateGenerator(this);
}
}
private GeneticSearchCandidateGenerator(Builder builder) {
super(builder.parameterSpace, builder.dataParameters, builder.initDone);
initialize();
chromosomeFactory = builder.chromosomeFactory;
populationModel = builder.populationModel;
selectionOperator = builder.selectionOperator;
chromosomeFactory.initializeInstance(builder.parameterSpace.numParameters());
populationModel.initializeInstance(builder.minimizeScore);
selectionOperator.initializeInstance(populationModel, chromosomeFactory);
}
@Override
public boolean hasMoreCandidates() {
return hasMoreCandidates;
}
@Override
public Candidate getCandidate() {
double[] values = null;
Object value = null;
Exception e = null;
try {
values = selectionOperator.buildNextGenes();
value = parameterSpace.getValue(values);
} catch (GeneticGenerationException e2) {
log.warn("Error generating candidate", e2);
e = e2;
hasMoreCandidates = false;
} catch (Exception e2) {
log.warn("Error getting configuration for candidate", e2);
e = e2;
}
return new Candidate(value, candidateCounter.getAndIncrement(), values, dataParameters, e);
}
@Override
public Class<?> getCandidateType() {
return null;
}
@Override
public String toString() {
return "GeneticSearchCandidateGenerator";
}
@Override
public void reportResults(OptimizationResult result) {
if (result.getScore() == null) {
return;
}
Chromosome newChromosome = chromosomeFactory.createChromosome(result.getCandidate().getFlatParameters(),
result.getScore());
populationModel.add(newChromosome);
}
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator;
import lombok.EqualsAndHashCode;
import lombok.Getter;
import lombok.extern.slf4j.Slf4j;
import org.apache.commons.math3.random.RandomAdaptor;
import org.deeplearning4j.arbiter.optimize.api.Candidate;
import org.deeplearning4j.arbiter.optimize.api.ParameterSpace;
import org.deeplearning4j.arbiter.optimize.parameter.FixedValue;
import org.deeplearning4j.arbiter.optimize.parameter.discrete.DiscreteParameterSpace;
import org.deeplearning4j.arbiter.optimize.parameter.integer.IntegerParameterSpace;
import org.deeplearning4j.arbiter.util.LeafUtils;
import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
import com.fasterxml.jackson.annotation.JsonProperty;
import java.util.*;
import java.util.concurrent.ConcurrentLinkedQueue;
/**
* GridSearchCandidateGenerator: generates candidates in an exhaustive grid search manner.<br>
* Note that:<br>
* - For discrete parameters: the grid size (# values to check per hyperparameter) is equal to the number of values for
* that hyperparameter<br>
* - For integer parameters: the grid size is equal to {@code min(discretizationCount,max-min+1)}. Some integer ranges can
* be large, and we don't necessarily want to exhaustively search them. {@code discretizationCount} is a constructor argument<br>
* - For continuous parameters: the grid size is equal to {@code discretizationCount}.<br>
* In all cases, the minimum, maximum and gridSize-2 values between the min/max will be generated.<br>
* Also note that: if a probability distribution is provided for continuous hyperparameters, this will be taken into account
* when generating candidates. This allows the grid for a hyperparameter to be non-linear: i.e., for example, linear in log space
*
* @author Alex Black
*/
@Slf4j
@EqualsAndHashCode(exclude = {"order"}, callSuper = true)
@JsonIgnoreProperties({"numValuesPerParam", "totalNumCandidates", "order", "candidateCounter", "rng", "candidate"})
public class GridSearchCandidateGenerator extends BaseCandidateGenerator {
/**
* In what order should candidates be generated?<br>
* <b>Sequential</b>: generate candidates in order. The first hyperparameter will be changed most rapidly, and the last
* will be changed least rapidly.<br>
* <b>RandomOrder</b>: generate candidates in a random order<br>
* In both cases, the same candidates will be generated; only the order of generation is different
*/
public enum Mode {
Sequential, RandomOrder
}
private final int discretizationCount;
private final Mode mode;
private int[] numValuesPerParam;
@Getter
private int totalNumCandidates;
private Queue<Integer> order;
/**
* @param parameterSpace ParameterSpace from which to generate candidates
* @param discretizationCount For continuous parameters: into how many values should we discretize them into?
* For example, suppose continuous parameter is in range [0,1] with 3 bins:
* do [0.0, 0.5, 1.0]. Note that if all values
* @param mode {@link GridSearchCandidateGenerator.Mode} specifies the order
* in which candidates should be generated.
*/
public GridSearchCandidateGenerator(@JsonProperty("parameterSpace") ParameterSpace<?> parameterSpace,
@JsonProperty("discretizationCount") int discretizationCount, @JsonProperty("mode") Mode mode,
@JsonProperty("dataParameters") Map<String, Object> dataParameters,
@JsonProperty("initDone") boolean initDone) {
super(parameterSpace, dataParameters, initDone);
this.discretizationCount = discretizationCount;
this.mode = mode;
initialize();
}
/**
* @param parameterSpace ParameterSpace from which to generate candidates
* @param discretizationCount For continuous parameters: into how many values should we discretize them into?
* For example, suppose continuous parameter is in range [0,1] with 3 bins:
* do [0.0, 0.5, 1.0]. Note that if all values
* @param mode {@link GridSearchCandidateGenerator.Mode} specifies the order
* in which candidates should be generated.
*/
public GridSearchCandidateGenerator(ParameterSpace<?> parameterSpace, int discretizationCount, Mode mode,
Map<String, Object> dataParameters){
this(parameterSpace, discretizationCount, mode, dataParameters, false);
}
@Override
protected void initialize() {
super.initialize();
List<ParameterSpace> leaves = LeafUtils.getUniqueObjects(parameterSpace.collectLeaves());
int nParams = leaves.size();
//Work out for each parameter: is it continuous or discrete?
// for grid search: discrete values are grid-searchable as-is
// continuous values: discretize using 'discretizationCount' bins
// integer values: use min(max-min+1, discretizationCount) values. i.e., discretize if necessary
numValuesPerParam = new int[nParams];
long searchSize = 1;
for (int i = 0; i < nParams; i++) {
ParameterSpace ps = leaves.get(i);
if (ps instanceof DiscreteParameterSpace) {
DiscreteParameterSpace dps = (DiscreteParameterSpace) ps;
numValuesPerParam[i] = dps.numValues();
} else if (ps instanceof IntegerParameterSpace) {
IntegerParameterSpace ips = (IntegerParameterSpace) ps;
int min = ips.getMin();
int max = ips.getMax();
//Discretize, as some integer ranges are much too large to search (i.e., num. neural network units, between 100 and 1000)
numValuesPerParam[i] = Math.min(max - min + 1, discretizationCount);
} else if (ps instanceof FixedValue){
numValuesPerParam[i] = 1;
} else {
numValuesPerParam[i] = discretizationCount;
}
searchSize *= numValuesPerParam[i];
}
if (searchSize >= Integer.MAX_VALUE)
throw new IllegalStateException("Invalid search: cannot process search with " + searchSize
+ " candidates > Integer.MAX_VALUE"); //TODO find a more reasonable upper bound?
order = new ConcurrentLinkedQueue<>();
totalNumCandidates = (int) searchSize;
switch (mode) {
case Sequential:
for (int i = 0; i < totalNumCandidates; i++) {
order.add(i);
}
break;
case RandomOrder:
List<Integer> tempList = new ArrayList<>(totalNumCandidates);
for (int i = 0; i < totalNumCandidates; i++) {
tempList.add(i);
}
Collections.shuffle(tempList, new RandomAdaptor(rng));
order.addAll(tempList);
break;
default:
throw new RuntimeException();
}
}
@Override
public boolean hasMoreCandidates() {
return !order.isEmpty();
}
@Override
public Candidate getCandidate() {
int next = order.remove();
//Next: max integer (candidate number) to values
double[] values = indexToValues(numValuesPerParam, next, totalNumCandidates);
Object value = null;
Exception e = null;
try {
value = parameterSpace.getValue(values);
} catch (Exception e2) {
log.warn("Error getting configuration for candidate", e2);
e = e2;
}
return new Candidate(value, candidateCounter.getAndIncrement(), values, dataParameters, e);
}
@Override
public Class<?> getCandidateType() {
return null;
}
public static double[] indexToValues(int[] numValuesPerParam, int candidateIdx, int product) {
//How? first map to index of num possible values. Then: to double values in range 0 to 1
// 0-> [0,0,0], 1-> [1,0,0], 2-> [2,0,0], 3-> [0,1,0] etc
//Based on: Nd4j Shape.ind2sub
int countNon1 = 0;
for( int i : numValuesPerParam)
if(i > 1)
countNon1++;
int denom = product;
int num = candidateIdx;
int[] index = new int[numValuesPerParam.length];
for (int i = index.length - 1; i >= 0; i--) {
denom /= numValuesPerParam[i];
index[i] = num / denom;
num %= denom;
}
//Now: convert indexes to values in range [0,1]
//min value -> 0
//max value -> 1
double[] out = new double[countNon1];
int outIdx = 0;
for (int i = 0; i < numValuesPerParam.length; i++) {
if (numValuesPerParam[i] > 1){
out[outIdx++] = index[i] / ((double) (numValuesPerParam[i] - 1));
}
}
return out;
}
@Override
public String toString() {
return "GridSearchCandidateGenerator(mode=" + mode + ")";
}
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator;
import lombok.EqualsAndHashCode;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.arbiter.optimize.api.Candidate;
import org.deeplearning4j.arbiter.optimize.api.ParameterSpace;
import com.fasterxml.jackson.annotation.JsonCreator;
import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
import com.fasterxml.jackson.annotation.JsonProperty;
import java.util.Map;
/**
* RandomSearchGenerator: generates candidates at random.<br>
* Note: if a probability distribution is provided for continuous hyperparameters,
* this will be taken into account
* when generating candidates. This allows the search to be weighted more towards
* certain values according to a probability
* density. For example: generate samples for learning rate according to log uniform distribution
*
* @author Alex Black
*/
@Slf4j
@EqualsAndHashCode(callSuper = true)
@JsonIgnoreProperties({"numValuesPerParam", "totalNumCandidates", "order", "candidateCounter", "rng", "candidate"})
public class RandomSearchGenerator extends BaseCandidateGenerator {
@JsonCreator
public RandomSearchGenerator(@JsonProperty("parameterSpace") ParameterSpace<?> parameterSpace,
@JsonProperty("dataParameters") Map<String, Object> dataParameters,
@JsonProperty("initDone") boolean initDone) {
super(parameterSpace, dataParameters, initDone);
initialize();
}
public RandomSearchGenerator(ParameterSpace<?> parameterSpace, Map<String,Object> dataParameters){
this(parameterSpace, dataParameters, false);
}
public RandomSearchGenerator(ParameterSpace<?> parameterSpace){
this(parameterSpace, null, false);
}
@Override
public boolean hasMoreCandidates() {
return true;
}
@Override
public Candidate getCandidate() {
double[] randomValues = new double[parameterSpace.numParameters()];
for (int i = 0; i < randomValues.length; i++)
randomValues[i] = rng.nextDouble();
Object value = null;
Exception e = null;
try {
value = parameterSpace.getValue(randomValues);
} catch (Exception e2) {
log.warn("Error getting configuration for candidate", e2);
e = e2;
}
return new Candidate(value, candidateCounter.getAndIncrement(), randomValues, dataParameters, e);
}
@Override
public Class<?> getCandidateType() {
return null;
}
@Override
public String toString() {
return "RandomSearchGenerator";
}
}

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/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator.genetic;
import lombok.Data;
/**
* Candidates are stored as Chromosome in the population model
*
* @author Alexandre Boulanger
*/
@Data
public class Chromosome {
/**
* The fitness score of the genes.
*/
protected final double fitness;
/**
* The genes.
*/
protected final double[] genes;
public Chromosome(double[] genes, double fitness) {
this.genes = genes;
this.fitness = fitness;
}
}

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/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator.genetic;
/**
* A factory that builds new chromosomes. Used by the GeneticSearchCandidateGenerator.
*
* @author Alexandre Boulanger
*/
public class ChromosomeFactory {
private int chromosomeLength;
/**
* Called by the GeneticSearchCandidateGenerator.
*/
public void initializeInstance(int chromosomeLength) {
this.chromosomeLength = chromosomeLength;
}
/**
* Create a new instance of a Chromosome
*
* @param genes The genes
* @param fitness The fitness score
* @return A new instance of Chromosome
*/
public Chromosome createChromosome(double[] genes, double fitness) {
return new Chromosome(genes, fitness);
}
/**
* @return The number of genes in a chromosome
*/
public int getChromosomeLength() {
return chromosomeLength;
}
}

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/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator.genetic.crossover;
import org.apache.commons.math3.random.JDKRandomGenerator;
import org.apache.commons.math3.random.RandomGenerator;
import org.apache.commons.math3.random.SynchronizedRandomGenerator;
import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.parentselection.RandomTwoParentSelection;
import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.parentselection.TwoParentSelection;
import org.nd4j.common.base.Preconditions;
/**
* A crossover operator that linearly combines the genes of two parents. <br>
* When a crossover is generated (with a of probability <i>crossover rate</i>), each genes is a linear combination of the corresponding genes of the parents.
* <p>
* <i>t*parentA + (1-t)*parentB, where t is [0, 1] and different for each gene.</i>
*
* @author Alexandre Boulanger
*/
public class ArithmeticCrossover extends TwoParentsCrossoverOperator {
private static final double DEFAULT_CROSSOVER_RATE = 0.85;
private final double crossoverRate;
private final RandomGenerator rng;
public static class Builder {
private double crossoverRate = DEFAULT_CROSSOVER_RATE;
private RandomGenerator rng;
private TwoParentSelection parentSelection;
/**
* The probability that the operator generates a crossover (default 0.85).
*
* @param rate A value between 0.0 and 1.0
*/
public Builder crossoverRate(double rate) {
Preconditions.checkState(rate >= 0.0 && rate <= 1.0, "Rate must be between 0.0 and 1.0, got %s", rate);
this.crossoverRate = rate;
return this;
}
/**
* Use a supplied RandomGenerator
*
* @param rng An instance of RandomGenerator
*/
public Builder randomGenerator(RandomGenerator rng) {
this.rng = rng;
return this;
}
/**
* The parent selection behavior. Default is random parent selection.
*
* @param parentSelection An instance of TwoParentSelection
*/
public Builder parentSelection(TwoParentSelection parentSelection) {
this.parentSelection = parentSelection;
return this;
}
public ArithmeticCrossover build() {
if (rng == null) {
rng = new SynchronizedRandomGenerator(new JDKRandomGenerator());
}
if (parentSelection == null) {
parentSelection = new RandomTwoParentSelection();
}
return new ArithmeticCrossover(this);
}
}
private ArithmeticCrossover(ArithmeticCrossover.Builder builder) {
super(builder.parentSelection);
this.crossoverRate = builder.crossoverRate;
this.rng = builder.rng;
}
/**
* Has a probability <i>crossoverRate</i> of performing the crossover where each gene is a linear combination of:<br>
* <i>t*parentA + (1-t)*parentB, where t is [0, 1] and different for each gene.</i><br>
* Otherwise, returns the genes of a random parent.
*
* @return The crossover result. See {@link CrossoverResult}.
*/
@Override
public CrossoverResult crossover() {
double[][] parents = parentSelection.selectParents();
double[] offspringValues = new double[parents[0].length];
if (rng.nextDouble() < crossoverRate) {
for (int i = 0; i < offspringValues.length; ++i) {
double t = rng.nextDouble();
offspringValues[i] = t * parents[0][i] + (1.0 - t) * parents[1][i];
}
return new CrossoverResult(true, offspringValues);
}
return new CrossoverResult(false, parents[0]);
}
}

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/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator.genetic.crossover;
import org.deeplearning4j.arbiter.optimize.generator.genetic.population.PopulationModel;
/**
* Abstract class for all crossover operators
*
* @author Alexandre Boulanger
*/
public abstract class CrossoverOperator {
protected PopulationModel populationModel;
/**
* Will be called by the selection operator once the population model is instantiated.
*/
public void initializeInstance(PopulationModel populationModel) {
this.populationModel = populationModel;
}
/**
* Performs the crossover
*
* @return The crossover result. See {@link CrossoverResult}.
*/
public abstract CrossoverResult crossover();
}

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/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator.genetic.crossover;
import lombok.Data;
/**
* Returned by a crossover operator
*
* @author Alexandre Boulanger
*/
@Data
public class CrossoverResult {
/**
* If false, there was no crossover and the operator simply returned the genes of a random parent.
* If true, the genes are the result of a crossover.
*/
private final boolean isModified;
/**
* The genes returned by the operator.
*/
private final double[] genes;
public CrossoverResult(boolean isModified, double[] genes) {
this.isModified = isModified;
this.genes = genes;
}
}

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/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator.genetic.crossover;
import org.apache.commons.math3.random.JDKRandomGenerator;
import org.apache.commons.math3.random.RandomGenerator;
import org.apache.commons.math3.random.SynchronizedRandomGenerator;
import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.parentselection.RandomTwoParentSelection;
import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.parentselection.TwoParentSelection;
import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.utils.CrossoverPointsGenerator;
import org.nd4j.common.base.Preconditions;
import java.util.Deque;
/**
* The K-Point crossover will select at random multiple crossover points.<br>
* Each gene comes from one of the two parents. Each time a crossover point is reached, the parent is switched.
*/
public class KPointCrossover extends TwoParentsCrossoverOperator {
private static final double DEFAULT_CROSSOVER_RATE = 0.85;
private static final int DEFAULT_MIN_CROSSOVER = 1;
private static final int DEFAULT_MAX_CROSSOVER = 4;
private final double crossoverRate;
private final int minCrossovers;
private final int maxCrossovers;
private final RandomGenerator rng;
public static class Builder {
private double crossoverRate = DEFAULT_CROSSOVER_RATE;
private int minCrossovers = DEFAULT_MIN_CROSSOVER;
private int maxCrossovers = DEFAULT_MAX_CROSSOVER;
private RandomGenerator rng;
private TwoParentSelection parentSelection;
/**
* The probability that the operator generates a crossover (default 0.85).
*
* @param rate A value between 0.0 and 1.0
*/
public Builder crossoverRate(double rate) {
Preconditions.checkState(rate >= 0.0 && rate <= 1.0, "Rate must be between 0.0 and 1.0, got %s", rate);
this.crossoverRate = rate;
return this;
}
/**
* The number of crossovers points (default is min 1, max 4)
*
* @param min The minimum number
* @param max The maximum number
*/
public Builder numCrossovers(int min, int max) {
Preconditions.checkState(max >= 0 && min >= 0, "Min and max must be positive");
Preconditions.checkState(max >= min, "Max must be greater or equal to min");
this.minCrossovers = min;
this.maxCrossovers = max;
return this;
}
/**
* Use a fixed number of crossover points
*
* @param num The number of crossovers
*/
public Builder numCrossovers(int num) {
Preconditions.checkState(num >= 0, "Num must be positive");
this.minCrossovers = num;
this.maxCrossovers = num;
return this;
}
/**
* Use a supplied RandomGenerator
*
* @param rng An instance of RandomGenerator
*/
public Builder randomGenerator(RandomGenerator rng) {
this.rng = rng;
return this;
}
/**
* The parent selection behavior. Default is random parent selection.
*
* @param parentSelection An instance of TwoParentSelection
*/
public Builder parentSelection(TwoParentSelection parentSelection) {
this.parentSelection = parentSelection;
return this;
}
public KPointCrossover build() {
if (rng == null) {
rng = new SynchronizedRandomGenerator(new JDKRandomGenerator());
}
if (parentSelection == null) {
parentSelection = new RandomTwoParentSelection();
}
return new KPointCrossover(this);
}
}
private CrossoverPointsGenerator crossoverPointsGenerator;
private KPointCrossover(KPointCrossover.Builder builder) {
super(builder.parentSelection);
this.crossoverRate = builder.crossoverRate;
this.maxCrossovers = builder.maxCrossovers;
this.minCrossovers = builder.minCrossovers;
this.rng = builder.rng;
}
private CrossoverPointsGenerator getCrossoverPointsGenerator(int chromosomeLength) {
if (crossoverPointsGenerator == null) {
crossoverPointsGenerator =
new CrossoverPointsGenerator(chromosomeLength, minCrossovers, maxCrossovers, rng);
}
return crossoverPointsGenerator;
}
/**
* Has a probability <i>crossoverRate</i> of performing the crossover where the operator will select at random multiple crossover points.<br>
* Each gene comes from one of the two parents. Each time a crossover point is reached, the parent is switched. <br>
* Otherwise, returns the genes of a random parent.
*
* @return The crossover result. See {@link CrossoverResult}.
*/
@Override
public CrossoverResult crossover() {
double[][] parents = parentSelection.selectParents();
boolean isModified = false;
double[] resultGenes = parents[0];
if (rng.nextDouble() < crossoverRate) {
// Select crossover points
Deque<Integer> crossoverPoints = getCrossoverPointsGenerator(parents[0].length).getCrossoverPoints();
// Crossover
resultGenes = new double[parents[0].length];
int currentParent = 0;
int nextCrossover = crossoverPoints.pop();
for (int i = 0; i < resultGenes.length; ++i) {
if (i == nextCrossover) {
currentParent = currentParent == 0 ? 1 : 0;
nextCrossover = crossoverPoints.pop();
}
resultGenes[i] = parents[currentParent][i];
}
isModified = true;
}
return new CrossoverResult(isModified, resultGenes);
}
}

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/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator.genetic.crossover;
import org.apache.commons.math3.random.JDKRandomGenerator;
import org.apache.commons.math3.random.RandomGenerator;
import org.apache.commons.math3.random.SynchronizedRandomGenerator;
import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.parentselection.RandomTwoParentSelection;
import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.parentselection.TwoParentSelection;
import org.nd4j.common.base.Preconditions;
/**
* The single point crossover will select a random point where every genes before that point comes from one parent
* and after which every genes comes from the other parent.
*
* @author Alexandre Boulanger
*/
public class SinglePointCrossover extends TwoParentsCrossoverOperator {
private static final double DEFAULT_CROSSOVER_RATE = 0.85;
private final RandomGenerator rng;
private final double crossoverRate;
public static class Builder {
private double crossoverRate = DEFAULT_CROSSOVER_RATE;
private RandomGenerator rng;
private TwoParentSelection parentSelection;
/**
* The probability that the operator generates a crossover (default 0.85).
*
* @param rate A value between 0.0 and 1.0
*/
public Builder crossoverRate(double rate) {
Preconditions.checkState(rate >= 0.0 && rate <= 1.0, "Rate must be between 0.0 and 1.0, got %s", rate);
this.crossoverRate = rate;
return this;
}
/**
* Use a supplied RandomGenerator
*
* @param rng An instance of RandomGenerator
*/
public Builder randomGenerator(RandomGenerator rng) {
this.rng = rng;
return this;
}
/**
* The parent selection behavior. Default is random parent selection.
*
* @param parentSelection An instance of TwoParentSelection
*/
public Builder parentSelection(TwoParentSelection parentSelection) {
this.parentSelection = parentSelection;
return this;
}
public SinglePointCrossover build() {
if (rng == null) {
rng = new SynchronizedRandomGenerator(new JDKRandomGenerator());
}
if (parentSelection == null) {
parentSelection = new RandomTwoParentSelection();
}
return new SinglePointCrossover(this);
}
}
private SinglePointCrossover(SinglePointCrossover.Builder builder) {
super(builder.parentSelection);
this.crossoverRate = builder.crossoverRate;
this.rng = builder.rng;
}
/**
* Has a probability <i>crossoverRate</i> of performing the crossover where the operator will select a random crossover point.<br>
* Each gene before this point comes from one of the two parents and each gene at or after this point comes from the other parent.
* Otherwise, returns the genes of a random parent.
*
* @return The crossover result. See {@link CrossoverResult}.
*/
public CrossoverResult crossover() {
double[][] parents = parentSelection.selectParents();
boolean isModified = false;
double[] resultGenes = parents[0];
if (rng.nextDouble() < crossoverRate) {
int chromosomeLength = parents[0].length;
// Crossover
resultGenes = new double[chromosomeLength];
int crossoverPoint = rng.nextInt(chromosomeLength);
for (int i = 0; i < resultGenes.length; ++i) {
resultGenes[i] = ((i < crossoverPoint) ? parents[0] : parents[1])[i];
}
isModified = true;
}
return new CrossoverResult(isModified, resultGenes);
}
}

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/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator.genetic.crossover;
import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.parentselection.TwoParentSelection;
import org.deeplearning4j.arbiter.optimize.generator.genetic.population.PopulationModel;
/**
* Abstract class for all crossover operators that applies to two parents.
*
* @author Alexandre Boulanger
*/
public abstract class TwoParentsCrossoverOperator extends CrossoverOperator {
protected final TwoParentSelection parentSelection;
/**
* @param parentSelection A parent selection that selects two parents.
*/
protected TwoParentsCrossoverOperator(TwoParentSelection parentSelection) {
this.parentSelection = parentSelection;
}
/**
* Will be called by the selection operator once the population model is instantiated.
*/
@Override
public void initializeInstance(PopulationModel populationModel) {
super.initializeInstance(populationModel);
parentSelection.initializeInstance(populationModel.getPopulation());
}
}

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/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator.genetic.crossover;
import org.apache.commons.math3.random.JDKRandomGenerator;
import org.apache.commons.math3.random.RandomGenerator;
import org.apache.commons.math3.random.SynchronizedRandomGenerator;
import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.parentselection.RandomTwoParentSelection;
import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.parentselection.TwoParentSelection;
import org.nd4j.common.base.Preconditions;
/**
* The uniform crossover will, for each gene, randomly select the parent that donates the gene.
*
* @author Alexandre Boulanger
*/
public class UniformCrossover extends TwoParentsCrossoverOperator {
private static final double DEFAULT_CROSSOVER_RATE = 0.85;
private static final double DEFAULT_PARENT_BIAS_FACTOR = 0.5;
private final double crossoverRate;
private final double parentBiasFactor;
private final RandomGenerator rng;
public static class Builder {
private double crossoverRate = DEFAULT_CROSSOVER_RATE;
private double parentBiasFactor = DEFAULT_PARENT_BIAS_FACTOR;
private RandomGenerator rng;
private TwoParentSelection parentSelection;
/**
* The probability that the operator generates a crossover (default 0.85).
*
* @param rate A value between 0.0 and 1.0
*/
public Builder crossoverRate(double rate) {
Preconditions.checkState(rate >= 0.0 && rate <= 1.0, "Rate must be between 0.0 and 1.0, got %s", rate);
this.crossoverRate = rate;
return this;
}
/**
* A factor that will introduce a bias in the parent selection.<br>
*
* @param factor In the range [0, 1]. 0 will only select the first parent while 1 only select the second one. The default is 0.5; no bias.
*/
public Builder parentBiasFactor(double factor) {
Preconditions.checkState(factor >= 0.0 && factor <= 1.0, "Factor must be between 0.0 and 1.0, got %s",
factor);
this.parentBiasFactor = factor;
return this;
}
/**
* Use a supplied RandomGenerator
*
* @param rng An instance of RandomGenerator
*/
public Builder randomGenerator(RandomGenerator rng) {
this.rng = rng;
return this;
}
/**
* The parent selection behavior. Default is random parent selection.
*
* @param parentSelection An instance of TwoParentSelection
*/
public Builder parentSelection(TwoParentSelection parentSelection) {
this.parentSelection = parentSelection;
return this;
}
public UniformCrossover build() {
if (rng == null) {
rng = new SynchronizedRandomGenerator(new JDKRandomGenerator());
}
if (parentSelection == null) {
parentSelection = new RandomTwoParentSelection();
}
return new UniformCrossover(this);
}
}
private UniformCrossover(UniformCrossover.Builder builder) {
super(builder.parentSelection);
this.crossoverRate = builder.crossoverRate;
this.parentBiasFactor = builder.parentBiasFactor;
this.rng = builder.rng;
}
/**
* Has a probability <i>crossoverRate</i> of performing the crossover where the operator will select randomly which parent donates the gene.<br>
* One of the parent may be favored if the bias is different than 0.5
* Otherwise, returns the genes of a random parent.
*
* @return The crossover result. See {@link CrossoverResult}.
*/
@Override
public CrossoverResult crossover() {
// select the parents
double[][] parents = parentSelection.selectParents();
double[] resultGenes = parents[0];
boolean isModified = false;
if (rng.nextDouble() < crossoverRate) {
// Crossover
resultGenes = new double[parents[0].length];
for (int i = 0; i < resultGenes.length; ++i) {
resultGenes[i] = ((rng.nextDouble() < parentBiasFactor) ? parents[0] : parents[1])[i];
}
isModified = true;
}
return new CrossoverResult(isModified, resultGenes);
}
}

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/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.parentselection;
import org.deeplearning4j.arbiter.optimize.generator.genetic.Chromosome;
import java.util.List;
/**
* Abstract class for all parent selection behaviors
*
* @author Alexandre Boulanger
*/
public abstract class ParentSelection {
protected List<Chromosome> population;
/**
* Will be called by the crossover operator once the population model is instantiated.
*/
public void initializeInstance(List<Chromosome> population) {
this.population = population;
}
/**
* Performs the parent selection
*
* @return An array of parents genes. The outer array are the parents, and the inner array are the genes.
*/
public abstract double[][] selectParents();
}

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/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.parentselection;
import org.apache.commons.math3.random.JDKRandomGenerator;
import org.apache.commons.math3.random.RandomGenerator;
import org.apache.commons.math3.random.SynchronizedRandomGenerator;
/**
* A parent selection behavior that returns two random parents.
*
* @author Alexandre Boulanger
*/
public class RandomTwoParentSelection extends TwoParentSelection {
private final RandomGenerator rng;
public RandomTwoParentSelection() {
this(new SynchronizedRandomGenerator(new JDKRandomGenerator()));
}
/**
* Use a supplied RandomGenerator
*
* @param rng An instance of RandomGenerator
*/
public RandomTwoParentSelection(RandomGenerator rng) {
this.rng = rng;
}
/**
* Selects two random parents
*
* @return An array of parents genes. The outer array are the parents, and the inner array are the genes.
*/
@Override
public double[][] selectParents() {
double[][] parents = new double[2][];
int parent1Idx = rng.nextInt(population.size());
int parent2Idx;
do {
parent2Idx = rng.nextInt(population.size());
} while (parent1Idx == parent2Idx);
parents[0] = population.get(parent1Idx).getGenes();
parents[1] = population.get(parent2Idx).getGenes();
return parents;
}
}

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/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.parentselection;
/**
* Abstract class for all parent selection behaviors that selects two parents.
*
* @author Alexandre Boulanger
*/
public abstract class TwoParentSelection extends ParentSelection {
}

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/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.utils;
import org.apache.commons.math3.random.RandomGenerator;
import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.KPointCrossover;
import java.util.*;
/**
* A helper class used by {@link KPointCrossover} to generate the crossover points
*
* @author Alexandre Boulanger
*/
public class CrossoverPointsGenerator {
private final int minCrossovers;
private final int maxCrossovers;
private final RandomGenerator rng;
private List<Integer> parameterIndexes;
/**
* Constructor
*
* @param chromosomeLength The number of genes
* @param minCrossovers The minimum number of crossover points to generate
* @param maxCrossovers The maximum number of crossover points to generate
* @param rng A RandomGenerator instance
*/
public CrossoverPointsGenerator(int chromosomeLength, int minCrossovers, int maxCrossovers, RandomGenerator rng) {
this.minCrossovers = minCrossovers;
this.maxCrossovers = maxCrossovers;
this.rng = rng;
parameterIndexes = new ArrayList<Integer>();
for (int i = 0; i < chromosomeLength; ++i) {
parameterIndexes.add(i);
}
}
/**
* Generate a list of crossover points.
*
* @return An ordered list of crossover point indexes and with Integer.MAX_VALUE as the last element
*/
public Deque<Integer> getCrossoverPoints() {
Collections.shuffle(parameterIndexes);
List<Integer> crossoverPointLists =
parameterIndexes.subList(0, rng.nextInt(maxCrossovers - minCrossovers) + minCrossovers);
Collections.sort(crossoverPointLists);
Deque<Integer> crossoverPoints = new ArrayDeque<Integer>(crossoverPointLists);
crossoverPoints.add(Integer.MAX_VALUE);
return crossoverPoints;
}
}

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/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator.genetic.culling;
import org.deeplearning4j.arbiter.optimize.generator.genetic.population.PopulationModel;
/**
* The cull operator will remove from the population the least desirables chromosomes.
*
* @author Alexandre Boulanger
*/
public interface CullOperator {
/**
* Will be called by the population model once created.
*/
void initializeInstance(PopulationModel populationModel);
/**
* Cull the population to the culled size.
*/
void cullPopulation();
/**
* @return The target population size after culling.
*/
int getCulledSize();
}

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/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator.genetic.culling;
/**
* An elitist cull operator that discards the chromosomes with the worst fitness while keeping the best ones.
*
* @author Alexandre Boulanger
*/
public class LeastFitCullOperator extends RatioCullOperator {
/**
* The default cull ratio is 1/3.
*/
public LeastFitCullOperator() {
super();
}
/**
* @param cullRatio The ratio of the maximum population size to be culled.<br>
* For example, a ratio of 1/3 on a population with a maximum size of 30 will cull back a given population to 20.
*/
public LeastFitCullOperator(double cullRatio) {
super(cullRatio);
}
/**
* Will discard the chromosomes with the worst fitness until the population size fall back at the culled size.
*/
@Override
public void cullPopulation() {
while (population.size() > culledSize) {
population.remove(population.size() - 1);
}
}
}

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/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator.genetic.culling;
import org.deeplearning4j.arbiter.optimize.generator.genetic.Chromosome;
import org.deeplearning4j.arbiter.optimize.generator.genetic.population.PopulationModel;
import org.nd4j.common.base.Preconditions;
import java.util.List;
/**
* An abstract base for cull operators that culls back the population to a ratio of its maximum size.
*
* @author Alexandre Boulanger
*/
public abstract class RatioCullOperator implements CullOperator {
private static final double DEFAULT_CULL_RATIO = 1.0 / 3.0;
protected int culledSize;
protected List<Chromosome> population;
protected final double cullRatio;
/**
* @param cullRatio The ratio of the maximum population size to be culled.<br>
* For example, a ratio of 1/3 on a population with a maximum size of 30 will cull back a given population to 20.
*/
public RatioCullOperator(double cullRatio) {
Preconditions.checkState(cullRatio >= 0.0 && cullRatio <= 1.0, "Cull ratio must be between 0.0 and 1.0, got %s",
cullRatio);
this.cullRatio = cullRatio;
}
/**
* The default cull ratio is 1/3
*/
public RatioCullOperator() {
this(DEFAULT_CULL_RATIO);
}
/**
* Will be called by the population model once created.
*/
public void initializeInstance(PopulationModel populationModel) {
this.population = populationModel.getPopulation();
culledSize = (int) (populationModel.getPopulationSize() * (1.0 - cullRatio) + 0.5);
}
/**
* @return The target population size after culling.
*/
@Override
public int getCulledSize() {
return culledSize;
}
}

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/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator.genetic.exceptions;
public class GeneticGenerationException extends RuntimeException {
public GeneticGenerationException(String message) {
super(message);
}
}

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/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator.genetic.mutation;
/**
* The mutation operator will apply a mutation to the given genes.
*
* @author Alexandre Boulanger
*/
public interface MutationOperator {
/**
* Performs a mutation.
*
* @param genes The genes to be mutated
* @return True if the genes were mutated, otherwise false.
*/
boolean mutate(double[] genes);
}

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/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator.genetic.mutation;
import org.apache.commons.math3.random.JDKRandomGenerator;
import org.apache.commons.math3.random.RandomGenerator;
import org.apache.commons.math3.random.SynchronizedRandomGenerator;
import org.nd4j.common.base.Preconditions;
/**
* A mutation operator where each gene has a chance of being mutated with a <i>mutation rate</i> probability.
*
* @author Alexandre Boulanger
*/
public class RandomMutationOperator implements MutationOperator {
private static final double DEFAULT_MUTATION_RATE = 0.005;
private final double mutationRate;
private final RandomGenerator rng;
public static class Builder {
private double mutationRate = DEFAULT_MUTATION_RATE;
private RandomGenerator rng;
/**
* Each gene will have this probability of being mutated.
*
* @param rate The mutation rate. (default 0.005)
*/
public Builder mutationRate(double rate) {
Preconditions.checkState(rate >= 0.0 && rate <= 1.0, "Rate must be between 0.0 and 1.0, got %s", rate);
this.mutationRate = rate;
return this;
}
/**
* Use a supplied RandomGenerator
*
* @param rng An instance of RandomGenerator
*/
public Builder randomGenerator(RandomGenerator rng) {
this.rng = rng;
return this;
}
public RandomMutationOperator build() {
if (rng == null) {
rng = new SynchronizedRandomGenerator(new JDKRandomGenerator());
}
return new RandomMutationOperator(this);
}
}
private RandomMutationOperator(RandomMutationOperator.Builder builder) {
this.mutationRate = builder.mutationRate;
this.rng = builder.rng;
}
/**
* Performs the mutation. Each gene has a <i>mutation rate</i> probability of being mutated.
*
* @param genes The genes to be mutated
* @return True if the genes were mutated, otherwise false.
*/
@Override
public boolean mutate(double[] genes) {
boolean hasMutation = false;
for (int i = 0; i < genes.length; ++i) {
if (rng.nextDouble() < mutationRate) {
genes[i] = rng.nextDouble();
hasMutation = true;
}
}
return hasMutation;
}
}

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/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator.genetic.population;
import org.deeplearning4j.arbiter.optimize.generator.genetic.Chromosome;
import java.util.ArrayList;
import java.util.List;
/**
* A population initializer that build an empty population.
*
* @author Alexandre Boulanger
*/
public class EmptyPopulationInitializer implements PopulationInitializer {
/**
* Initialize an empty population
*
* @param size The maximum size of the population.
* @return The initialized population.
*/
@Override
public List<Chromosome> getInitializedPopulation(int size) {
return new ArrayList<>(size);
}
}

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/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator.genetic.population;
import org.deeplearning4j.arbiter.optimize.generator.genetic.Chromosome;
import java.util.List;
/**
* An initializer that construct the population used by the population model.
*
* @author Alexandre Boulanger
*/
public interface PopulationInitializer {
/**
* Called by the population model to construct the population
*
* @param size The maximum size of the population
* @return An initialized population
*/
List<Chromosome> getInitializedPopulation(int size);
}

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/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator.genetic.population;
import org.deeplearning4j.arbiter.optimize.generator.genetic.Chromosome;
import java.util.List;
/**
* A listener that is called when the population changes.
*
* @author Alexandre Boulanger
*/
public interface PopulationListener {
/**
* Called after the population has changed.
*
* @param population The population after it has changed.
*/
void onChanged(List<Chromosome> population);
}

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/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator.genetic.population;
import lombok.Getter;
import org.deeplearning4j.arbiter.optimize.generator.genetic.Chromosome;
import org.deeplearning4j.arbiter.optimize.generator.genetic.culling.CullOperator;
import org.deeplearning4j.arbiter.optimize.generator.genetic.culling.LeastFitCullOperator;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Comparator;
import java.util.List;
/**
* The population model handles all aspects of the population (initialization, additions and culling)
*
* @author Alexandre Boulanger
*/
public class PopulationModel {
private static final int DEFAULT_POPULATION_SIZE = 30;
private final CullOperator cullOperator;
private final List<PopulationListener> populationListeners = new ArrayList<>();
private Comparator<Chromosome> chromosomeComparator;
/**
* The maximum population size
*/
@Getter
private final int populationSize;
/**
* The population
*/
@Getter
public final List<Chromosome> population;
/**
* A comparator used when higher fitness value is better
*/
public static class MaximizeScoreComparator implements Comparator<Chromosome> {
@Override
public int compare(Chromosome lhs, Chromosome rhs) {
return -Double.compare(lhs.getFitness(), rhs.getFitness());
}
}
/**
* A comparator used when lower fitness value is better
*/
public static class MinimizeScoreComparator implements Comparator<Chromosome> {
@Override
public int compare(Chromosome lhs, Chromosome rhs) {
return Double.compare(lhs.getFitness(), rhs.getFitness());
}
}
public static class Builder {
private int populationSize = DEFAULT_POPULATION_SIZE;
private PopulationInitializer populationInitializer;
private CullOperator cullOperator;
/**
* Use an alternate population initialization behavior. Default is empty population.
*
* @param populationInitializer An instance of PopulationInitializer
*/
public Builder populationInitializer(PopulationInitializer populationInitializer) {
this.populationInitializer = populationInitializer;
return this;
}
/**
* The maximum population size. <br>
* If using a ratio based culling, using a population with culled size of around 1.5 to 2 times the number of genes generally gives good results.
* (e.g. For a chromosome having 10 genes, the culled size should be between 15 and 20. And with a cull ratio of 1/3 we should set the population size to 23 to 30. (15 / (1 - 1/3)), rounded up)
*
* @param size The maximum size of the population
*/
public Builder populationSize(int size) {
populationSize = size;
return this;
}
/**
* Use an alternate cull operator behavior. Default is least fit culling.
*
* @param cullOperator An instance of a CullOperator
*/
public Builder cullOperator(CullOperator cullOperator) {
this.cullOperator = cullOperator;
return this;
}
public PopulationModel build() {
if (cullOperator == null) {
cullOperator = new LeastFitCullOperator();
}
if (populationInitializer == null) {
populationInitializer = new EmptyPopulationInitializer();
}
return new PopulationModel(this);
}
}
public PopulationModel(PopulationModel.Builder builder) {
populationSize = builder.populationSize;
population = new ArrayList<>(builder.populationSize);
PopulationInitializer populationInitializer = builder.populationInitializer;
List<Chromosome> initializedPopulation = populationInitializer.getInitializedPopulation(populationSize);
population.clear();
population.addAll(initializedPopulation);
cullOperator = builder.cullOperator;
cullOperator.initializeInstance(this);
}
/**
* Called by the GeneticSearchCandidateGenerator
*/
public void initializeInstance(boolean minimizeScore) {
chromosomeComparator = minimizeScore ? new MinimizeScoreComparator() : new MaximizeScoreComparator();
}
/**
* Add a PopulationListener to the list of change listeners
* @param listener A PopulationListener instance
*/
public void addListener(PopulationListener listener) {
populationListeners.add(listener);
}
/**
* Add a Chromosome to the population and call the PopulationListeners. Culling may be triggered.
*
* @param element The chromosome to be added
*/
public void add(Chromosome element) {
if (population.size() == populationSize) {
cullOperator.cullPopulation();
}
population.add(element);
Collections.sort(population, chromosomeComparator);
triggerPopulationChangedListeners(population);
}
/**
* @return Return false when the population is below the culled size, otherwise true. <br>
* Used by the selection operator to know if the population is still too small and should generate random genes.
*/
public boolean isReadyToBreed() {
return population.size() >= cullOperator.getCulledSize();
}
private void triggerPopulationChangedListeners(List<Chromosome> population) {
for (PopulationListener listener : populationListeners) {
listener.onChanged(population);
}
}
}

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/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator.genetic.selection;
import org.apache.commons.math3.random.JDKRandomGenerator;
import org.apache.commons.math3.random.RandomGenerator;
import org.apache.commons.math3.random.SynchronizedRandomGenerator;
import org.deeplearning4j.arbiter.optimize.generator.genetic.ChromosomeFactory;
import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.CrossoverOperator;
import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.CrossoverResult;
import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.SinglePointCrossover;
import org.deeplearning4j.arbiter.optimize.generator.genetic.exceptions.GeneticGenerationException;
import org.deeplearning4j.arbiter.optimize.generator.genetic.mutation.MutationOperator;
import org.deeplearning4j.arbiter.optimize.generator.genetic.mutation.RandomMutationOperator;
import org.deeplearning4j.arbiter.optimize.generator.genetic.population.PopulationModel;
import java.util.Arrays;
/**
* A selection operator that will generate random genes initially. Once the population has reached the culled size,
* will start to generate offsprings of parents selected in the population.
*
* @author Alexandre Boulanger
*/
public class GeneticSelectionOperator extends SelectionOperator {
private final static int PREVIOUS_GENES_TO_KEEP = 100;
private final static int MAX_NUM_GENERATION_ATTEMPTS = 1024;
private final CrossoverOperator crossoverOperator;
private final MutationOperator mutationOperator;
private final RandomGenerator rng;
private double[][] previousGenes = new double[PREVIOUS_GENES_TO_KEEP][];
private int previousGenesIdx = 0;
public static class Builder {
private ChromosomeFactory chromosomeFactory;
private PopulationModel populationModel;
private CrossoverOperator crossoverOperator;
private MutationOperator mutationOperator;
private RandomGenerator rng;
/**
* Use an alternate crossover behavior. Default is SinglePointCrossover.
*
* @param crossoverOperator An instance of CrossoverOperator
*/
public Builder crossoverOperator(CrossoverOperator crossoverOperator) {
this.crossoverOperator = crossoverOperator;
return this;
}
/**
* Use an alternate mutation behavior. Default is RandomMutationOperator.
*
* @param mutationOperator An instance of MutationOperator
*/
public Builder mutationOperator(MutationOperator mutationOperator) {
this.mutationOperator = mutationOperator;
return this;
}
/**
* Use a supplied RandomGenerator
*
* @param rng An instance of RandomGenerator
*/
public Builder randomGenerator(RandomGenerator rng) {
this.rng = rng;
return this;
}
public GeneticSelectionOperator build() {
if (crossoverOperator == null) {
crossoverOperator = new SinglePointCrossover.Builder().build();
}
if (mutationOperator == null) {
mutationOperator = new RandomMutationOperator.Builder().build();
}
if (rng == null) {
rng = new SynchronizedRandomGenerator(new JDKRandomGenerator());
}
return new GeneticSelectionOperator(crossoverOperator, mutationOperator, rng);
}
}
private GeneticSelectionOperator(CrossoverOperator crossoverOperator, MutationOperator mutationOperator,
RandomGenerator rng) {
this.crossoverOperator = crossoverOperator;
this.mutationOperator = mutationOperator;
this.rng = rng;
}
/**
* Called by GeneticSearchCandidateGenerator
*/
@Override
public void initializeInstance(PopulationModel populationModel, ChromosomeFactory chromosomeFactory) {
super.initializeInstance(populationModel, chromosomeFactory);
crossoverOperator.initializeInstance(populationModel);
}
/**
* Build a new set of genes. Has two distinct modes of operation
* <ul>
* <li>Before the population has reached the culled size: will return a random set of genes.</li>
* <li>After: Parents will be selected among the population, a crossover will be applied followed by a mutation.</li>
* </ul>
* @return Returns the generated set of genes
* @throws GeneticGenerationException If buildNextGenes() can't generate a set that has not already been tried,
* or if the crossover and the mutation operators can't generate a set,
* this exception is thrown.
*/
@Override
public double[] buildNextGenes() {
double[] result;
boolean hasAlreadyBeenTried;
int attemptsRemaining = MAX_NUM_GENERATION_ATTEMPTS;
do {
if (populationModel.isReadyToBreed()) {
result = buildOffspring();
} else {
result = buildRandomGenes();
}
hasAlreadyBeenTried = hasAlreadyBeenTried(result);
if (hasAlreadyBeenTried && --attemptsRemaining == 0) {
throw new GeneticGenerationException("Failed to generate a set of genes not already tried.");
}
} while (hasAlreadyBeenTried);
previousGenes[previousGenesIdx] = result;
previousGenesIdx = ++previousGenesIdx % previousGenes.length;
return result;
}
private boolean hasAlreadyBeenTried(double[] genes) {
for (int i = 0; i < previousGenes.length; ++i) {
double[] current = previousGenes[i];
if (current != null && Arrays.equals(current, genes)) {
return true;
}
}
return false;
}
private double[] buildOffspring() {
double[] offspringValues;
boolean isModified;
int attemptsRemaining = MAX_NUM_GENERATION_ATTEMPTS;
do {
CrossoverResult crossoverResult = crossoverOperator.crossover();
offspringValues = crossoverResult.getGenes();
isModified = crossoverResult.isModified();
isModified |= mutationOperator.mutate(offspringValues);
if (!isModified && --attemptsRemaining == 0) {
throw new GeneticGenerationException(
String.format("Crossover and mutation operators failed to generate a new set of genes after %s attempts.",
MAX_NUM_GENERATION_ATTEMPTS));
}
} while (!isModified);
return offspringValues;
}
private double[] buildRandomGenes() {
double[] randomValues = new double[chromosomeFactory.getChromosomeLength()];
for (int i = 0; i < randomValues.length; ++i) {
randomValues[i] = rng.nextDouble();
}
return randomValues;
}
}

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/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator.genetic.selection;
import org.deeplearning4j.arbiter.optimize.generator.genetic.ChromosomeFactory;
import org.deeplearning4j.arbiter.optimize.generator.genetic.population.PopulationModel;
/**
* An abstract class for all selection operators. Used by the GeneticSearchCandidateGenerator to generate new candidates.
*
* @author Alexandre Boulanger
*/
public abstract class SelectionOperator {
protected PopulationModel populationModel;
protected ChromosomeFactory chromosomeFactory;
/**
* Called by GeneticSearchCandidateGenerator
*/
public void initializeInstance(PopulationModel populationModel, ChromosomeFactory chromosomeFactory) {
this.populationModel = populationModel;
this.chromosomeFactory = chromosomeFactory;
}
/**
* Generate a new set of genes.
*/
public abstract double[] buildNextGenes();
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.generator.util;
import org.nd4j.common.function.Supplier;
import java.io.*;
public class SerializedSupplier<T> implements Serializable, Supplier<T> {
private byte[] asBytes;
public SerializedSupplier(T obj){
try(ByteArrayOutputStream baos = new ByteArrayOutputStream(); ObjectOutputStream oos = new ObjectOutputStream(baos)){
oos.writeObject(obj);
oos.flush();
oos.close();
asBytes = baos.toByteArray();
} catch (Exception e){
throw new RuntimeException("Error serializing object - must be serializable",e);
}
}
@Override
public T get() {
try(ObjectInputStream ois = new ObjectInputStream(new ByteArrayInputStream(asBytes))){
return (T)ois.readObject();
} catch (Exception e){
throw new RuntimeException("Error deserializing object",e);
}
}
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.parameter;
import lombok.EqualsAndHashCode;
import org.deeplearning4j.arbiter.optimize.api.ParameterSpace;
import java.util.Collections;
import java.util.List;
import java.util.Map;
/**
* BooleanParameterSpace is a {@code ParameterSpace<Boolean>}; Defines {True, False} as a parameter space
* If argument to setValue is less than or equal to 0.5 it will return True else False
*
* @author susaneraly
*/
@EqualsAndHashCode
public class BooleanSpace implements ParameterSpace<Boolean> {
private int index = -1;
@Override
public Boolean getValue(double[] input) {
if (index == -1) {
throw new IllegalStateException("Cannot get value: ParameterSpace index has not been set");
}
if (input[index] <= 0.5) return Boolean.TRUE;
else return Boolean.FALSE;
}
@Override
public int numParameters() {
return 1;
}
@Override
public List<ParameterSpace> collectLeaves() {
return Collections.singletonList((ParameterSpace) this);
}
@Override
public Map<String, ParameterSpace> getNestedSpaces() {
return Collections.emptyMap();
}
@Override
public boolean isLeaf() {
return true;
}
@Override
public void setIndices(int... indices) {
if (indices == null || indices.length != 1)
throw new IllegalArgumentException("Invalid index");
this.index = indices[0];
}
@Override
public String toString() {
return "BooleanSpace()";
}
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.parameter;
import lombok.EqualsAndHashCode;
import lombok.Getter;
import org.deeplearning4j.arbiter.optimize.api.ParameterSpace;
import org.deeplearning4j.arbiter.optimize.serde.jackson.FixedValueDeserializer;
import org.deeplearning4j.arbiter.optimize.serde.jackson.FixedValueSerializer;
import org.deeplearning4j.arbiter.util.ObjectUtils;
import com.fasterxml.jackson.annotation.JsonCreator;
import com.fasterxml.jackson.annotation.JsonProperty;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
import com.fasterxml.jackson.databind.annotation.JsonDeserialize;
import com.fasterxml.jackson.databind.annotation.JsonSerialize;
import java.util.Collections;
import java.util.List;
import java.util.Map;
/**
* FixedValue is a ParameterSpace that defines only a single fixed value
*
* @param <T> Type of (fixed) value
*/
@EqualsAndHashCode
@JsonSerialize(using = FixedValueSerializer.class)
@JsonDeserialize(using = FixedValueDeserializer.class)
@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
public class FixedValue<T> implements ParameterSpace<T> {
@Getter
private Object value;
private int index;
@JsonCreator
public FixedValue(@JsonProperty("value") T value) {
this.value = value;
}
@Override
public String toString() {
return "FixedValue(" + ObjectUtils.valueToString(value) + ")";
}
@Override
public T getValue(double[] input) {
return (T) value;
}
@Override
public int numParameters() {
return 0;
}
@Override
public List<ParameterSpace> collectLeaves() {
return Collections.emptyList();
}
@Override
public Map<String, ParameterSpace> getNestedSpaces() {
return Collections.emptyMap();
}
@Override
public boolean isLeaf() {
return true;
}
@Override
public void setIndices(int... indices) {
if (indices != null && indices.length != 0)
throw new IllegalArgumentException(
"Invalid call: FixedValue ParameterSpace " + "should not be given an index (0 params)");
}
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.parameter.continuous;
import org.apache.commons.math3.distribution.RealDistribution;
import org.apache.commons.math3.distribution.UniformRealDistribution;
import org.deeplearning4j.arbiter.optimize.api.ParameterSpace;
import org.deeplearning4j.arbiter.optimize.distribution.DistributionUtils;
import org.deeplearning4j.arbiter.optimize.serde.jackson.RealDistributionDeserializer;
import org.deeplearning4j.arbiter.optimize.serde.jackson.RealDistributionSerializer;
import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
import com.fasterxml.jackson.annotation.JsonProperty;
import com.fasterxml.jackson.databind.annotation.JsonDeserialize;
import com.fasterxml.jackson.databind.annotation.JsonSerialize;
import java.util.Collections;
import java.util.List;
import java.util.Map;
/**
* ContinuousParametSpace is a {@code ParameterSpace<Double>} that (optionally) takes an Apache Commons
* {@link RealDistribution} when used for random sampling (such as in a RandomSearchCandidateGenerator)
*
* @author Alex Black
*/
public class ContinuousParameterSpace implements ParameterSpace<Double> {
//Need to use custom serializers/deserializers for commons RealDistribution instances
@JsonSerialize(using = RealDistributionSerializer.class)
@JsonDeserialize(using = RealDistributionDeserializer.class)
private RealDistribution distribution;
private int index = -1;
/**
* ContinuousParameterSpace with uniform distribution between the minimum and maximum values
*
* @param min Minimum value that can be generated
* @param max Maximum value that can be generated
*/
public ContinuousParameterSpace(double min, double max) {
this(new UniformRealDistribution(min, max));
}
/**
* ConditiousParameterSpcae wiht a specified probability distribution. The provided distribution defines the min/max
* values, and (for random search, etc) will be used when generating random values
*
* @param distribution Distribution to sample from
*/
public ContinuousParameterSpace(@JsonProperty("distribution") RealDistribution distribution) {
this.distribution = distribution;
}
@Override
public Double getValue(double[] input) {
if (index == -1) {
throw new IllegalStateException("Cannot get value: ParameterSpace index has not been set");
}
return distribution.inverseCumulativeProbability(input[index]);
}
@Override
public int numParameters() {
return 1;
}
@Override
public List<ParameterSpace> collectLeaves() {
return Collections.singletonList((ParameterSpace) this);
}
@Override
public Map<String, ParameterSpace> getNestedSpaces() {
return Collections.emptyMap();
}
@Override
public boolean isLeaf() {
return true;
}
@Override
public void setIndices(int... indices) {
if (indices == null || indices.length != 1) {
throw new IllegalArgumentException("Invalid index");
}
this.index = indices[0];
}
@Override
public String toString() {
if (distribution instanceof UniformRealDistribution) {
return "ContinuousParameterSpace(min=" + distribution.getSupportLowerBound() + ",max="
+ distribution.getSupportUpperBound() + ")";
} else {
return "ContinuousParameterSpace(" + distribution + ")";
}
}
public boolean equals(Object o) {
if (o == this)
return true;
if (!(o instanceof ContinuousParameterSpace))
return false;
final ContinuousParameterSpace other = (ContinuousParameterSpace) o;
if (distribution == null ? other.distribution != null
: !DistributionUtils.distributionsEqual(distribution, other.distribution))
return false;
if (this.index != other.index)
return false;
return true;
}
public int hashCode() {
final int PRIME = 59;
int result = 1;
result = result * PRIME + (distribution == null ? 43 : distribution.getClass().hashCode());
result = result * PRIME + this.index;
return result;
}
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.parameter.discrete;
import lombok.EqualsAndHashCode;
import org.deeplearning4j.arbiter.optimize.api.ParameterSpace;
import org.deeplearning4j.arbiter.util.ObjectUtils;
import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
import com.fasterxml.jackson.annotation.JsonProperty;
import com.fasterxml.jackson.databind.annotation.JsonSerialize;
import java.util.*;
/**
* A DiscreteParameterSpace is used for a set of un-ordered values
*
* @param <P> Parameter type
* @author Alex Black
*/
@EqualsAndHashCode
public class DiscreteParameterSpace<P> implements ParameterSpace<P> {
@JsonSerialize
private List<P> values;
private int index = -1;
public DiscreteParameterSpace(@JsonProperty("values") P... values) {
if (values != null)
this.values = Arrays.asList(values);
}
public DiscreteParameterSpace(Collection<P> values) {
this.values = new ArrayList<>(values);
}
public int numValues() {
return values.size();
}
@Override
public P getValue(double[] input) {
if (index == -1) {
throw new IllegalStateException("Cannot get value: ParameterSpace index has not been set");
}
if (values == null)
throw new IllegalStateException("Values are null.");
//Map a value in range [0,1] to one of the list of values
//First value: [0,width], second: (width,2*width], third: (3*width,4*width] etc
int size = values.size();
if (size == 1)
return values.get(0);
double width = 1.0 / size;
int val = (int) (input[index] / width);
return values.get(Math.min(val, size - 1));
}
@Override
public int numParameters() {
return 1;
}
@Override
public List<ParameterSpace> collectLeaves() {
return Collections.singletonList((ParameterSpace) this);
}
@Override
public Map<String, ParameterSpace> getNestedSpaces() {
return Collections.emptyMap();
}
@Override
public boolean isLeaf() {
return true;
}
@Override
public void setIndices(int... indices) {
if (indices == null || indices.length != 1) {
throw new IllegalArgumentException("Invalid index");
}
this.index = indices[0];
}
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("DiscreteParameterSpace(");
int n = values.size();
for (int i = 0; i < n; i++) {
P value = values.get(i);
sb.append(ObjectUtils.valueToString(value));
sb.append((i == n - 1 ? ")" : ","));
}
return sb.toString();
}
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.parameter.integer;
import lombok.NoArgsConstructor;
import org.apache.commons.math3.distribution.IntegerDistribution;
import org.apache.commons.math3.distribution.UniformIntegerDistribution;
import org.deeplearning4j.arbiter.optimize.api.ParameterSpace;
import org.deeplearning4j.arbiter.optimize.distribution.DistributionUtils;
import org.deeplearning4j.arbiter.optimize.serde.jackson.IntegerDistributionDeserializer;
import org.deeplearning4j.arbiter.optimize.serde.jackson.IntegerDistributionSerializer;
import com.fasterxml.jackson.annotation.JsonCreator;
import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
import com.fasterxml.jackson.annotation.JsonProperty;
import com.fasterxml.jackson.databind.annotation.JsonDeserialize;
import com.fasterxml.jackson.databind.annotation.JsonSerialize;
import java.util.Collections;
import java.util.List;
import java.util.Map;
/**
* IntegerParameterSpace is a {@code ParameterSpace<Integer>}; i.e., defines an ordered space of integers between
* some minimum and maximum value
*
* @author Alex Black
*/
@JsonIgnoreProperties({"min", "max"})
@NoArgsConstructor
public class IntegerParameterSpace implements ParameterSpace<Integer> {
@JsonSerialize(using = IntegerDistributionSerializer.class)
@JsonDeserialize(using = IntegerDistributionDeserializer.class)
private IntegerDistribution distribution;
private int index = -1;
/**
* Create an IntegerParameterSpace with a uniform distribution between the specified min/max (inclusive)
*
* @param min Min value, inclusive
* @param max Max value, inclusive
*/
public IntegerParameterSpace(int min, int max) {
this(new UniformIntegerDistribution(min, max));
}
/**
* Crate an IntegerParametSpace from the given IntegerDistribution
*
* @param distribution Distribution to use
*/
@JsonCreator
public IntegerParameterSpace(@JsonProperty("distribution") IntegerDistribution distribution) {
this.distribution = distribution;
}
public int getMin() {
return distribution.getSupportLowerBound();
}
public int getMax() {
return distribution.getSupportUpperBound();
}
@Override
public Integer getValue(double[] input) {
if (index == -1) {
throw new IllegalStateException("Cannot get value: ParameterSpace index has not been set");
}
return distribution.inverseCumulativeProbability(input[index]);
}
@Override
public int numParameters() {
return 1;
}
@Override
public List<ParameterSpace> collectLeaves() {
return Collections.singletonList((ParameterSpace) this);
}
@Override
public Map<String, ParameterSpace> getNestedSpaces() {
return Collections.emptyMap();
}
@Override
public boolean isLeaf() {
return true;
}
@Override
public void setIndices(int... indices) {
if (indices == null || indices.length != 1)
throw new IllegalArgumentException("Invalid index");
this.index = indices[0];
}
@Override
public String toString() {
if (distribution instanceof UniformIntegerDistribution) {
return "IntegerParameterSpace(min=" + distribution.getSupportLowerBound() + ",max="
+ distribution.getSupportUpperBound() + ")";
} else {
return "IntegerParameterSpace(" + distribution + ")";
}
}
public boolean equals(Object o) {
if (o == this)
return true;
if (!(o instanceof IntegerParameterSpace))
return false;
final IntegerParameterSpace other = (IntegerParameterSpace) o;
if (!other.canEqual(this))
return false;
if (distribution == null ? other.distribution != null
: !DistributionUtils.distributionEquals(distribution, other.distribution))
return false;
if (this.index != other.index)
return false;
return true;
}
public int hashCode() {
final int PRIME = 59;
int result = 1;
result = result * PRIME + (distribution == null ? 43 : distribution.getClass().hashCode());
result = result * PRIME + this.index;
return result;
}
protected boolean canEqual(Object other) {
return other instanceof IntegerParameterSpace;
}
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.parameter.math;
import org.deeplearning4j.arbiter.optimize.api.AbstractParameterSpace;
import org.deeplearning4j.arbiter.optimize.api.ParameterSpace;
import java.util.List;
/**
* A simple parameter space that implements scalar mathematical operations on another parameter space. This allows you
* to do things like Y = X * 2, where X is a parameter space. For example, a layer size hyperparameter could be set
* using this to 2x the size of the previous layer
*
* @param <T> Type of the parameter space
* @author Alex Black
*/
public class MathOp<T extends Number> extends AbstractParameterSpace<T> {
private ParameterSpace<T> parameterSpace;
private Op op;
private T scalar;
public MathOp(ParameterSpace<T> parameterSpace, Op op, T scalar){
this.parameterSpace = parameterSpace;
this.op = op;
this.scalar = scalar;
}
@Override
public T getValue(double[] parameterValues) {
T u = parameterSpace.getValue(parameterValues);
return op.doOp(u, scalar);
}
@Override
public int numParameters() {
return parameterSpace.numParameters();
}
@Override
public List<ParameterSpace> collectLeaves() {
return parameterSpace.collectLeaves();
}
@Override
public boolean isLeaf() {
return false;
}
@Override
public void setIndices(int... indices) {
parameterSpace.setIndices(indices);
}
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.parameter.math;
public enum Op {
ADD, SUB, MUL, DIV;
//Package private
<T extends Number> T doOp(T first, T second){
if(first instanceof Integer || first instanceof Long){
long result;
switch (this){
case ADD:
result = Long.valueOf(first.longValue() + second.longValue());
break;
case SUB:
result = Long.valueOf(first.longValue() - second.longValue());
break;
case MUL:
result = Long.valueOf(first.longValue() * second.longValue());
break;
case DIV:
result = Long.valueOf(first.longValue() / second.longValue());
break;
default:
throw new UnsupportedOperationException("Unknown op: " + this);
}
if(first instanceof Long){
return (T)Long.valueOf(result);
} else {
return (T)Integer.valueOf((int)result);
}
} else if(first instanceof Double || first instanceof Float){
double result;
switch (this){
case ADD:
result = Double.valueOf(first.doubleValue() + second.doubleValue());
break;
case SUB:
result = Double.valueOf(first.doubleValue() - second.doubleValue());
break;
case MUL:
result = Double.valueOf(first.doubleValue() * second.doubleValue());
break;
case DIV:
result = Double.valueOf(first.doubleValue() / second.doubleValue());
break;
default:
throw new UnsupportedOperationException("Unknown op: " + this);
}
if(first instanceof Double){
return (T)Double.valueOf(result);
} else {
return (T)Float.valueOf((float)result);
}
} else {
throw new UnsupportedOperationException("Not supported type: only Integer, Long, Double, Float supported" +
" here. Got type: " + first.getClass());
}
}
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.parameter.math;
import org.deeplearning4j.arbiter.optimize.api.AbstractParameterSpace;
import org.deeplearning4j.arbiter.optimize.api.ParameterSpace;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
/**
* A simple parameter space that implements pairwise mathematical operations on another parameter space. This allows you
* to do things like Z = X + Y, where X and Y are parameter spaces.
*
* @param <T> Type of the parameter space
* @author Alex Black
*/
public class PairMathOp<T extends Number> extends AbstractParameterSpace<T> {
private ParameterSpace<T> first;
private ParameterSpace<T> second;
private Op op;
public PairMathOp(ParameterSpace<T> first, ParameterSpace<T> second, Op op){
this.first = first;
this.second = second;
this.op = op;
}
@Override
public T getValue(double[] parameterValues) {
T f = first.getValue(parameterValues);
T s = second.getValue(parameterValues);
return op.doOp(f, s);
}
@Override
public int numParameters() {
return first.numParameters() + second.numParameters();
}
@Override
public List<ParameterSpace> collectLeaves() {
List<ParameterSpace> l = new ArrayList<>();
l.addAll(first.collectLeaves());
l.addAll(second.collectLeaves());
return l;
}
@Override
public boolean isLeaf() {
return false;
}
@Override
public void setIndices(int... indices) {
int n1 = first.numParameters();
int n2 = second.numParameters();
int[] s1 = Arrays.copyOfRange(indices, 0, n1);
int[] s2 = Arrays.copyOfRange(indices, n1, n1+n2);
first.setIndices(s1);
second.setIndices(s2);
}
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.runner;
import com.google.common.util.concurrent.ListenableFuture;
import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.extern.slf4j.Slf4j;
import org.apache.commons.lang3.exception.ExceptionUtils;
import org.deeplearning4j.arbiter.optimize.api.Candidate;
import org.deeplearning4j.arbiter.optimize.api.OptimizationResult;
import org.deeplearning4j.arbiter.optimize.api.data.DataProvider;
import org.deeplearning4j.arbiter.optimize.api.data.DataSource;
import org.deeplearning4j.arbiter.optimize.api.saving.ResultReference;
import org.deeplearning4j.arbiter.optimize.api.score.ScoreFunction;
import org.deeplearning4j.arbiter.optimize.api.termination.TerminationCondition;
import org.deeplearning4j.arbiter.optimize.config.OptimizationConfiguration;
import org.deeplearning4j.arbiter.optimize.runner.listener.StatusListener;
import java.util.*;
import java.util.concurrent.*;
import java.util.concurrent.atomic.AtomicInteger;
import java.util.concurrent.atomic.AtomicLong;
/**
* BaseOptimization runner: responsible for scheduling tasks, saving results using the result saver, etc.
*
* @author Alex Black
*/
@Slf4j
public abstract class BaseOptimizationRunner implements IOptimizationRunner {
private static final int POLLING_FREQUENCY = 1;
private static final TimeUnit POLLING_FREQUENCY_UNIT = TimeUnit.SECONDS;
protected OptimizationConfiguration config;
protected Queue<Future<OptimizationResult>> queuedFutures = new ConcurrentLinkedQueue<>();
protected BlockingQueue<Future<OptimizationResult>> completedFutures = new LinkedBlockingQueue<>();
protected AtomicInteger totalCandidateCount = new AtomicInteger();
protected AtomicInteger numCandidatesCompleted = new AtomicInteger();
protected AtomicInteger numCandidatesFailed = new AtomicInteger();
protected Double bestScore = null;
protected Long bestScoreTime = null;
protected AtomicInteger bestScoreCandidateIndex = new AtomicInteger(-1);
protected List<ResultReference> allResults = new ArrayList<>();
protected Map<Integer, CandidateInfo> currentStatus = new ConcurrentHashMap<>(); //TODO: better design possible?
protected ExecutorService futureListenerExecutor;
protected List<StatusListener> statusListeners = new ArrayList<>();
protected BaseOptimizationRunner(OptimizationConfiguration config) {
this.config = config;
if (config.getTerminationConditions() == null || config.getTerminationConditions().size() == 0) {
throw new IllegalArgumentException("Cannot create BaseOptimizationRunner without TerminationConditions ("
+ "termination conditions are null or empty)");
}
}
protected void init() {
futureListenerExecutor = Executors.newFixedThreadPool(maxConcurrentTasks(), new ThreadFactory() {
private AtomicLong counter = new AtomicLong(0);
@Override
public Thread newThread(Runnable r) {
Thread t = Executors.defaultThreadFactory().newThread(r);
t.setDaemon(true);
t.setName("ArbiterOptimizationRunner-" + counter.getAndIncrement());
return t;
}
});
}
/**
*
*/
@Override
public void execute() {
log.info("{}: execution started", this.getClass().getSimpleName());
config.setExecutionStartTime(System.currentTimeMillis());
for (StatusListener listener : statusListeners) {
listener.onInitialization(this);
}
//Initialize termination conditions (start timers, etc)
for (TerminationCondition c : config.getTerminationConditions()) {
c.initialize(this);
}
//Queue initial tasks:
List<Future<OptimizationResult>> tempList = new ArrayList<>(100);
while (true) {
//Otherwise: add tasks if required
Future<OptimizationResult> future = null;
try {
future = completedFutures.poll(POLLING_FREQUENCY, POLLING_FREQUENCY_UNIT);
} catch (InterruptedException e) {
//No op?
}
if (future != null) {
tempList.add(future);
}
completedFutures.drainTo(tempList);
//Process results (if any)
for (Future<OptimizationResult> f : tempList) {
queuedFutures.remove(f);
processReturnedTask(f);
}
if (tempList.size() > 0) {
for (StatusListener sl : statusListeners) {
sl.onRunnerStatusChange(this);
}
}
tempList.clear();
//Check termination conditions:
if (terminate()) {
shutdown(true);
break;
}
//Add additional tasks
while (config.getCandidateGenerator().hasMoreCandidates() && queuedFutures.size() < maxConcurrentTasks()) {
Candidate candidate = config.getCandidateGenerator().getCandidate();
CandidateInfo status;
if (candidate.getException() != null) {
//Failed on generation...
status = processFailedCandidates(candidate);
} else {
long created = System.currentTimeMillis();
ListenableFuture<OptimizationResult> f;
if(config.getDataSource() != null){
f = execute(candidate, config.getDataSource(), config.getDataSourceProperties(), config.getScoreFunction());
} else {
f = execute(candidate, config.getDataProvider(), config.getScoreFunction());
}
f.addListener(new OnCompletionListener(f), futureListenerExecutor);
queuedFutures.add(f);
totalCandidateCount.getAndIncrement();
status = new CandidateInfo(candidate.getIndex(), CandidateStatus.Created, null,
created, null, null, candidate.getFlatParameters(), null);
currentStatus.put(candidate.getIndex(), status);
}
for (StatusListener listener : statusListeners) {
listener.onCandidateStatusChange(status, this, null);
}
}
}
//Process any final (completed) tasks:
completedFutures.drainTo(tempList);
for (Future<OptimizationResult> f : tempList) {
queuedFutures.remove(f);
processReturnedTask(f);
}
tempList.clear();
log.info("Optimization runner: execution complete");
for (StatusListener listener : statusListeners) {
listener.onShutdown(this);
}
}
private CandidateInfo processFailedCandidates(Candidate<?> candidate) {
//In case the candidate fails during the creation of the candidate
long time = System.currentTimeMillis();
String stackTrace = ExceptionUtils.getStackTrace(candidate.getException());
CandidateInfo newStatus = new CandidateInfo(candidate.getIndex(), CandidateStatus.Failed, null, time, time,
time, candidate.getFlatParameters(), stackTrace);
currentStatus.put(candidate.getIndex(), newStatus);
return newStatus;
}
/**
* Process returned task (either completed or failed
*/
private void processReturnedTask(Future<OptimizationResult> future) {
long currentTime = System.currentTimeMillis();
OptimizationResult result;
try {
result = future.get(100, TimeUnit.MILLISECONDS);
} catch (InterruptedException e) {
throw new RuntimeException("Unexpected InterruptedException thrown for task", e);
} catch (ExecutionException e) {
//Note that most of the time, an OptimizationResult is returned even for an exception
//This is just to handle any that are missed there (or, by implementations that don't properly do this)
log.warn("Task failed", e);
numCandidatesFailed.getAndIncrement();
return;
} catch (TimeoutException e) {
throw new RuntimeException(e); //TODO
}
//Update internal status:
CandidateInfo status = currentStatus.get(result.getIndex());
CandidateInfo newStatus = new CandidateInfo(result.getIndex(), result.getCandidateInfo().getCandidateStatus(),
result.getScore(), status.getCreatedTime(), result.getCandidateInfo().getStartTime(),
currentTime, status.getFlatParams(), result.getCandidateInfo().getExceptionStackTrace());
currentStatus.put(result.getIndex(), newStatus);
//Listeners (on complete, etc) should be executed in underlying task
if (result.getCandidateInfo().getCandidateStatus() == CandidateStatus.Failed) {
log.info("Task {} failed during execution: {}", result.getIndex(), result.getCandidateInfo().getExceptionStackTrace());
numCandidatesFailed.getAndIncrement();
} else {
//Report completion to candidate generator
config.getCandidateGenerator().reportResults(result);
Double score = result.getScore();
log.info("Completed task {}, score = {}", result.getIndex(), result.getScore());
boolean minimize = config.getScoreFunction().minimize();
if (score != null && (bestScore == null
|| ((minimize && score < bestScore) || (!minimize && score > bestScore)))) {
if (bestScore == null) {
log.info("New best score: {} (first completed model)", score);
} else {
int idx = result.getIndex();
int lastBestIdx = bestScoreCandidateIndex.get();
log.info("New best score: {}, model {} (prev={}, model {})", score, idx, bestScore, lastBestIdx);
}
bestScore = score;
bestScoreTime = System.currentTimeMillis();
bestScoreCandidateIndex.set(result.getIndex());
}
numCandidatesCompleted.getAndIncrement();
//Model saving is done in the optimization tasks, to avoid CUDA threading issues
ResultReference resultReference = result.getResultReference();
if (resultReference != null)
allResults.add(resultReference);
}
}
@Override
public int numCandidatesTotal() {
return totalCandidateCount.get();
}
@Override
public int numCandidatesCompleted() {
return numCandidatesCompleted.get();
}
@Override
public int numCandidatesFailed() {
return numCandidatesFailed.get();
}
@Override
public int numCandidatesQueued() {
return queuedFutures.size();
}
@Override
public Double bestScore() {
return bestScore;
}
@Override
public Long bestScoreTime() {
return bestScoreTime;
}
@Override
public int bestScoreCandidateIndex() {
return bestScoreCandidateIndex.get();
}
@Override
public List<ResultReference> getResults() {
return new ArrayList<>(allResults);
}
@Override
public OptimizationConfiguration getConfiguration() {
return config;
}
@Override
public void addListeners(StatusListener... listeners) {
for (StatusListener l : listeners) {
if (!statusListeners.contains(l)) {
statusListeners.add(l);
}
}
}
@Override
public void removeListeners(StatusListener... listeners) {
for (StatusListener l : listeners) {
if (statusListeners.contains(l)) {
statusListeners.remove(l);
}
}
}
@Override
public void removeAllListeners() {
statusListeners.clear();
}
@Override
public List<CandidateInfo> getCandidateStatus() {
List<CandidateInfo> list = new ArrayList<>();
list.addAll(currentStatus.values());
return list;
}
private boolean terminate() {
for (TerminationCondition c : config.getTerminationConditions()) {
if (c.terminate(this)) {
log.info("BaseOptimizationRunner global termination condition hit: {}", c);
return true;
}
}
return false;
}
@AllArgsConstructor
@Data
private class FutureDetails {
private final Future<OptimizationResult> future;
private final long startTime;
private final int index;
}
@AllArgsConstructor
private class OnCompletionListener implements Runnable {
private Future<OptimizationResult> future;
@Override
public void run() {
completedFutures.add(future);
}
}
protected abstract int maxConcurrentTasks();
@Deprecated
protected abstract ListenableFuture<OptimizationResult> execute(Candidate candidate, DataProvider dataProvider,
ScoreFunction scoreFunction);
@Deprecated
protected abstract List<ListenableFuture<OptimizationResult>> execute(List<Candidate> candidates,
DataProvider dataProvider, ScoreFunction scoreFunction);
protected abstract ListenableFuture<OptimizationResult> execute(Candidate candidate, Class<? extends DataSource> dataSource,
Properties dataSourceProperties, ScoreFunction scoreFunction);
protected abstract List<ListenableFuture<OptimizationResult>> execute(List<Candidate> candidates, Class<? extends DataSource> dataSource,
Properties dataSourceProperties, ScoreFunction scoreFunction);
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.runner;
import lombok.AllArgsConstructor;
import lombok.Data;
/**
* Simple helper class to store status of a candidate that is/has been/will be executed
*/
@AllArgsConstructor
@Data
public class CandidateInfo {
public CandidateInfo() {
//No arg constructor for Jackson
}
private int index;
private CandidateStatus candidateStatus;
private Double score;
private long createdTime;
private Long startTime;
private Long endTime;
private double[] flatParams; //Same as parameters in Candidate class
private String exceptionStackTrace;
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.runner;
/**
* Status for candidates
*/
public enum CandidateStatus {
Created, Running, Complete, Failed, Cancelled
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.runner;
import org.deeplearning4j.arbiter.optimize.api.saving.ResultReference;
import org.deeplearning4j.arbiter.optimize.config.OptimizationConfiguration;
import org.deeplearning4j.arbiter.optimize.runner.listener.StatusListener;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
import java.util.List;
@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
public interface IOptimizationRunner {
void execute();
/** Total number of candidates: created (scheduled), completed and failed */
int numCandidatesTotal();
int numCandidatesCompleted();
int numCandidatesFailed();
/** Number of candidates running or queued */
int numCandidatesQueued();
/** Best score found so far */
Double bestScore();
/** Time that the best score was found at, or 0 if no jobs have completed successfully */
Long bestScoreTime();
/** Index of the best scoring candidate, or -1 if no candidate has scored yet*/
int bestScoreCandidateIndex();
List<ResultReference> getResults();
OptimizationConfiguration getConfiguration();
void addListeners(StatusListener... listeners);
void removeListeners(StatusListener... listeners);
void removeAllListeners();
List<CandidateInfo> getCandidateStatus();
/**
* @param awaitCompletion If true: await completion of currently scheduled tasks. If false: shutdown immediately,
* cancelling any currently executing tasks
*/
void shutdown(boolean awaitCompletion);
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.runner;
import com.google.common.util.concurrent.ListenableFuture;
import com.google.common.util.concurrent.ListeningExecutorService;
import com.google.common.util.concurrent.MoreExecutors;
import lombok.Setter;
import org.deeplearning4j.arbiter.optimize.api.*;
import org.deeplearning4j.arbiter.optimize.api.data.DataProvider;
import org.deeplearning4j.arbiter.optimize.api.data.DataSource;
import org.deeplearning4j.arbiter.optimize.api.score.ScoreFunction;
import org.deeplearning4j.arbiter.optimize.config.OptimizationConfiguration;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import java.util.Properties;
import java.util.concurrent.*;
import java.util.concurrent.atomic.AtomicLong;
/**
* LocalOptimizationRunner: execute hyperparameter optimization
* locally (on current machine, in current JVM).
*
* @author Alex Black
*/
public class LocalOptimizationRunner extends BaseOptimizationRunner {
public static final int DEFAULT_MAX_CONCURRENT_TASKS = 1;
private final int maxConcurrentTasks;
private TaskCreator taskCreator;
private ListeningExecutorService executor;
@Setter
private long shutdownMaxWaitMS = 2L * 24 * 60 * 60 * 1000;
public LocalOptimizationRunner(OptimizationConfiguration config){
this(config, null);
}
public LocalOptimizationRunner(OptimizationConfiguration config, TaskCreator taskCreator) {
this(DEFAULT_MAX_CONCURRENT_TASKS, config, taskCreator);
}
public LocalOptimizationRunner(int maxConcurrentTasks, OptimizationConfiguration config){
this(maxConcurrentTasks, config, null);
}
public LocalOptimizationRunner(int maxConcurrentTasks, OptimizationConfiguration config, TaskCreator taskCreator) {
super(config);
if (maxConcurrentTasks <= 0)
throw new IllegalArgumentException("maxConcurrentTasks must be > 0 (got: " + maxConcurrentTasks + ")");
this.maxConcurrentTasks = maxConcurrentTasks;
if(taskCreator == null){
Class<? extends ParameterSpace> psClass = config.getCandidateGenerator().getParameterSpace().getClass();
taskCreator = TaskCreatorProvider.defaultTaskCreatorFor(psClass);
if(taskCreator == null){
throw new IllegalStateException("No TaskCreator was provided and a default TaskCreator cannot be " +
"inferred for ParameterSpace class " + psClass.getName() + ". Please provide a TaskCreator " +
"via the LocalOptimizationRunner constructor");
}
}
this.taskCreator = taskCreator;
ExecutorService exec = Executors.newFixedThreadPool(maxConcurrentTasks, new ThreadFactory() {
private AtomicLong counter = new AtomicLong(0);
@Override
public Thread newThread(Runnable r) {
Thread t = Executors.defaultThreadFactory().newThread(r);
t.setDaemon(true);
t.setName("LocalCandidateExecutor-" + counter.getAndIncrement());
return t;
}
});
executor = MoreExecutors.listeningDecorator(exec);
init();
}
@Override
protected int maxConcurrentTasks() {
return maxConcurrentTasks;
}
@Override
protected ListenableFuture<OptimizationResult> execute(Candidate candidate, DataProvider dataProvider,
ScoreFunction scoreFunction) {
return execute(Collections.singletonList(candidate), dataProvider, scoreFunction).get(0);
}
@Override
protected List<ListenableFuture<OptimizationResult>> execute(List<Candidate> candidates, DataProvider dataProvider,
ScoreFunction scoreFunction) {
List<ListenableFuture<OptimizationResult>> list = new ArrayList<>(candidates.size());
for (Candidate candidate : candidates) {
Callable<OptimizationResult> task =
taskCreator.create(candidate, dataProvider, scoreFunction, statusListeners, this);
list.add(executor.submit(task));
}
return list;
}
@Override
protected ListenableFuture<OptimizationResult> execute(Candidate candidate, Class<? extends DataSource> dataSource, Properties dataSourceProperties, ScoreFunction scoreFunction) {
return execute(Collections.singletonList(candidate), dataSource, dataSourceProperties, scoreFunction).get(0);
}
@Override
protected List<ListenableFuture<OptimizationResult>> execute(List<Candidate> candidates, Class<? extends DataSource> dataSource, Properties dataSourceProperties, ScoreFunction scoreFunction) {
List<ListenableFuture<OptimizationResult>> list = new ArrayList<>(candidates.size());
for (Candidate candidate : candidates) {
Callable<OptimizationResult> task = taskCreator.create(candidate, dataSource, dataSourceProperties, scoreFunction, statusListeners, this);
list.add(executor.submit(task));
}
return list;
}
@Override
public void shutdown(boolean awaitTermination) {
if(awaitTermination){
try {
executor.shutdown();
executor.awaitTermination(shutdownMaxWaitMS, TimeUnit.MILLISECONDS);
} catch (InterruptedException e){
throw new RuntimeException(e);
}
} else {
executor.shutdownNow();
}
}
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.runner.listener;
import org.deeplearning4j.arbiter.optimize.api.OptimizationResult;
import org.deeplearning4j.arbiter.optimize.runner.CandidateInfo;
import org.deeplearning4j.arbiter.optimize.runner.IOptimizationRunner;
/**
* BaseStatusListener: implements all methods of {@link StatusListener} as no-op.
* Users can extend this and override only the methods actually required
*
* @author Alex Black
*/
public abstract class BaseStatusListener implements StatusListener{
@Override
public void onInitialization(IOptimizationRunner runner) {
//No op
}
@Override
public void onShutdown(IOptimizationRunner runner) {
//No op
}
@Override
public void onRunnerStatusChange(IOptimizationRunner runner) {
//No op
}
@Override
public void onCandidateStatusChange(CandidateInfo candidateInfo, IOptimizationRunner runner, OptimizationResult result) {
//No op
}
@Override
public void onCandidateIteration(CandidateInfo candidateInfo, Object candidate, int iteration) {
//No op
}
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.runner.listener;
/**
* Created by Alex on 20/07/2017.
*/
public enum StatusChangeType {
CandidateCompleted, CandidateFailed, CandidateNewScheduled, CandidateNewBestScore
}

60
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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.runner.listener;
import org.deeplearning4j.arbiter.optimize.api.OptimizationResult;
import org.deeplearning4j.arbiter.optimize.runner.CandidateInfo;
import org.deeplearning4j.arbiter.optimize.runner.IOptimizationRunner;
/**
* The status Listener interface is used to inspect/track the status of execution, both for individual candidates,
* and for the optimisation runner overall.
*
* @author Alex Black
*/
public interface StatusListener {
/** Called when optimization runner starts execution */
void onInitialization(IOptimizationRunner runner);
/** Called when optimization runner terminates */
void onShutdown(IOptimizationRunner runner);
/** Called when any of the summary stats change, for the optimization runner:
* number scheduled, number completed, number failed, best score, etc. */
void onRunnerStatusChange(IOptimizationRunner runner);
/**
* Called when the status of the candidate is change. For example created, completed, failed.
*
* @param candidateInfo Candidate information
* @param runner Optimisation runner calling this method
* @param result Optimisation result. Maybe null.
*/
void onCandidateStatusChange(CandidateInfo candidateInfo, IOptimizationRunner runner, OptimizationResult result);
/**
* This method may be called by tasks as they are executing. The intent of this method is to report partial results,
* such as different stages of learning, or scores/evaluations so far
*
* @param candidateInfo Candidate information
* @param candidate Current candidate value/configuration
* @param iteration Current iteration number
*/
void onCandidateIteration(CandidateInfo candidateInfo, Object candidate, int iteration);
}

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.runner.listener.impl;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.arbiter.optimize.api.OptimizationResult;
import org.deeplearning4j.arbiter.optimize.runner.CandidateInfo;
import org.deeplearning4j.arbiter.optimize.runner.IOptimizationRunner;
import org.deeplearning4j.arbiter.optimize.runner.listener.StatusListener;
/**
* Created by Alex on 20/07/2017.
*/
@Slf4j
public class LoggingStatusListener implements StatusListener {
@Override
public void onInitialization(IOptimizationRunner runner) {
log.info("Optimization runner: initialized");
}
@Override
public void onShutdown(IOptimizationRunner runner) {
log.info("Optimization runner: shut down");
}
@Override
public void onRunnerStatusChange(IOptimizationRunner runner) {
log.info("Optimization runner: status change");
}
@Override
public void onCandidateStatusChange(CandidateInfo candidateInfo, IOptimizationRunner runner,
OptimizationResult result) {
log.info("Candidate status change: {}", candidateInfo);
}
@Override
public void onCandidateIteration(CandidateInfo candidateInfo, Object candidate, int iteration) {
log.info("Candidate iteration #{} - {}", iteration, candidate);
}
}

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package org.deeplearning4j.arbiter.optimize.serde.jackson;
import org.apache.commons.codec.binary.Base64;
import org.deeplearning4j.arbiter.optimize.parameter.FixedValue;
import com.fasterxml.jackson.core.JsonParser;
import com.fasterxml.jackson.core.JsonProcessingException;
import com.fasterxml.jackson.databind.DeserializationContext;
import com.fasterxml.jackson.databind.JsonDeserializer;
import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.ObjectMapper;
import java.io.ByteArrayInputStream;
import java.io.IOException;
import java.io.ObjectInputStream;
/**
* A custom deserializer to be used in conjunction with {@link FixedValueSerializer}
* @author Alex Black
*/
public class FixedValueDeserializer extends JsonDeserializer<FixedValue> {
@Override
public FixedValue deserialize(JsonParser p, DeserializationContext deserializationContext) throws IOException, JsonProcessingException {
JsonNode node = p.getCodec().readTree(p);
String className = node.get("@valueclass").asText();
Class<?> c;
try {
c = Class.forName(className);
} catch (Exception e) {
throw new RuntimeException(e);
}
if(node.has("value")){
//Number, String, Enum
JsonNode valueNode = node.get("value");
Object o = new ObjectMapper().treeToValue(valueNode, c);
return new FixedValue<>(o);
} else {
//Everything else
JsonNode valueNode = node.get("data");
String data = valueNode.asText();
byte[] b = new Base64().decode(data);
ObjectInputStream ois = new ObjectInputStream(new ByteArrayInputStream(b));
try {
Object o = ois.readObject();
return new FixedValue<>(o);
} catch (Throwable t) {
throw new RuntimeException(t);
}
}
}
}

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arbiter/arbiter-core/src/main/java/org/deeplearning4j/arbiter/optimize/serde/jackson/FixedValueSerializer.java Normal file
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package org.deeplearning4j.arbiter.optimize.serde.jackson;
import org.apache.commons.net.util.Base64;
import org.deeplearning4j.arbiter.optimize.parameter.FixedValue;
import com.fasterxml.jackson.core.JsonGenerator;
import com.fasterxml.jackson.core.type.WritableTypeId;
import com.fasterxml.jackson.databind.JsonSerializer;
import com.fasterxml.jackson.databind.SerializerProvider;
import com.fasterxml.jackson.databind.jsontype.TypeSerializer;
import java.io.ByteArrayOutputStream;
import java.io.IOException;
import java.io.ObjectOutputStream;
import static com.fasterxml.jackson.core.JsonToken.START_OBJECT;
/**
* A custom serializer to handle arbitrary object types
* Uses standard JSON where safe (number, string, enumerations) or Java object serialization (bytes -&gt; base64)
* The latter is not an ideal approach, but Jackson doesn't support serialization/deserialization of arbitrary
* objects very well
*
* @author Alex Black
*/
public class FixedValueSerializer extends JsonSerializer<FixedValue> {
@Override
public void serialize(FixedValue fixedValue, JsonGenerator j, SerializerProvider serializerProvider) throws IOException {
Object o = fixedValue.getValue();
j.writeStringField("@valueclass", o.getClass().getName());
if(o instanceof Number || o instanceof String || o instanceof Enum){
j.writeObjectField("value", o);
} else {
ByteArrayOutputStream baos = new ByteArrayOutputStream();
ObjectOutputStream oos = new ObjectOutputStream(baos);
oos.writeObject(o);
baos.close();
byte[] b = baos.toByteArray();
String base64 = new Base64().encodeToString(b);
j.writeStringField("data", base64);
}
}
@Override
public void serializeWithType(FixedValue value, JsonGenerator gen, SerializerProvider serializers, TypeSerializer typeSer) throws IOException {
WritableTypeId typeId = typeSer.typeId(value, START_OBJECT);
typeSer.writeTypePrefix(gen, typeId);
serialize(value, gen, serializers);
typeSer.writeTypeSuffix(gen, typeId);
}
}

59
arbiter/arbiter-core/src/main/java/org/deeplearning4j/arbiter/optimize/serde/jackson/IntegerDistributionDeserializer.java Normal file
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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.serde.jackson;
import org.apache.commons.math3.distribution.*;
import com.fasterxml.jackson.core.JsonParser;
import com.fasterxml.jackson.databind.DeserializationContext;
import com.fasterxml.jackson.databind.JsonDeserializer;
import com.fasterxml.jackson.databind.JsonNode;
import java.io.IOException;
/**
* Custom Jackson deserializer for integer distributions
*
* @author Alex Black
*/
public class IntegerDistributionDeserializer extends JsonDeserializer<IntegerDistribution> {
@Override
public IntegerDistribution deserialize(JsonParser p, DeserializationContext ctxt) throws IOException {
JsonNode node = p.getCodec().readTree(p);
String simpleName = node.get("distribution").asText();
switch (simpleName) {
case "BinomialDistribution":
return new BinomialDistribution(node.get("trials").asInt(), node.get("p").asDouble());
case "GeometricDistribution":
return new GeometricDistribution(node.get("p").asDouble());
case "HypergeometricDistribution":
return new HypergeometricDistribution(node.get("populationSize").asInt(),
node.get("numberOfSuccesses").asInt(), node.get("sampleSize").asInt());
case "PascalDistribution":
return new PascalDistribution(node.get("r").asInt(), node.get("p").asDouble());
case "PoissonDistribution":
return new PoissonDistribution(node.get("p").asDouble());
case "UniformIntegerDistribution":
return new UniformIntegerDistribution(node.get("lower").asInt(), node.get("upper").asInt());
case "ZipfDistribution":
return new ZipfDistribution(node.get("numElements").asInt(), node.get("exponent").asDouble());
default:
throw new RuntimeException("Unknown or not supported distribution: " + simpleName);
}
}
}

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arbiter/arbiter-core/src/main/java/org/deeplearning4j/arbiter/optimize/serde/jackson/IntegerDistributionSerializer.java Normal file
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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.optimize.serde.jackson;
import org.apache.commons.math3.distribution.*;
import com.fasterxml.jackson.core.JsonGenerator;
import com.fasterxml.jackson.databind.JsonSerializer;
import com.fasterxml.jackson.databind.SerializerProvider;
import java.io.IOException;
/**
* Custom Jackson serializer for integer distributions
*
* @author Alex Black
*/
public class IntegerDistributionSerializer extends JsonSerializer<IntegerDistribution> {
@Override
public void serialize(IntegerDistribution d, JsonGenerator j, SerializerProvider serializerProvider)
throws IOException {
Class<?> c = d.getClass();
String s = c.getSimpleName();
j.writeStartObject();
j.writeStringField("distribution", s);
if (c == BinomialDistribution.class) {
BinomialDistribution bd = (BinomialDistribution) d;
j.writeNumberField("trials", bd.getNumberOfTrials());
j.writeNumberField("p", bd.getProbabilityOfSuccess());
} else if (c == GeometricDistribution.class) {
GeometricDistribution gd = (GeometricDistribution) d;
j.writeNumberField("p", gd.getProbabilityOfSuccess());
} else if (c == HypergeometricDistribution.class) {
HypergeometricDistribution hd = (HypergeometricDistribution) d;
j.writeNumberField("populationSize", hd.getPopulationSize());
j.writeNumberField("numberOfSuccesses", hd.getNumberOfSuccesses());
j.writeNumberField("sampleSize", hd.getSampleSize());
} else if (c == PascalDistribution.class) {
PascalDistribution pd = (PascalDistribution) d;
j.writeNumberField("r", pd.getNumberOfSuccesses());
j.writeNumberField("p", pd.getProbabilityOfSuccess());
} else if (c == PoissonDistribution.class) {
PoissonDistribution pd = (PoissonDistribution) d;
j.writeNumberField("p", pd.getMean());
} else if (c == UniformIntegerDistribution.class) {
UniformIntegerDistribution ud = (UniformIntegerDistribution) d;
j.writeNumberField("lower", ud.getSupportLowerBound());
j.writeNumberField("upper", ud.getSupportUpperBound());
} else if (c == ZipfDistribution.class) {
ZipfDistribution zd = (ZipfDistribution) d;
j.writeNumberField("numElements", zd.getNumberOfElements());
j.writeNumberField("exponent", zd.getExponent());
} else {
throw new UnsupportedOperationException("Unknown or not supported IntegerDistribution: " + c);
}
j.writeEndObject();
}
}

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