Multi-Label Embedding Classification (scikit-multilearn#140)

support clems, lnemlc and scikit-learn based embeddings

Author: niedakh

.travis.yml

@@ -19,13 +19,14 @@ matrix:
           env: MEKA_CLASSPATH=/Users/travis/MEKA/meka/lib/
 
 script:
-- if [[ "$TOXENV" == "linux_py2" ]]; then docker exec -it scikit_multilearn_dev_test_docker python -m pytest /home/python-dev/repo; fi
-- if [[ "$TOXENV" == "linux_py3" ]]; then docker exec -it scikit_multilearn_dev_test_docker python3 -m pytest /home/python-dev/repo; fi
-- if [[ "$TRAVIS_OS_NAME" == "osx" ]];   then python3 -m pytest $TRAVIS_BUILD_DIR; fi
-- if [[ "$TRAVIS_OS_NAME" == "osx" ]];   then python -m pytest $TRAVIS_BUILD_DIR; fi
+- if [[ "$TOXENV" == "linux_py2" ]]; then docker exec -it scikit_multilearn_dev_test_docker python2 -m pytest -p no:warnings  /home/python-dev/repo; fi
+- if [[ "$TOXENV" == "linux_py3" ]]; then docker exec -it scikit_multilearn_dev_test_docker python3 -m pytest -p no:warnings  /home/python-dev/repo; fi
+- if [[ "$TRAVIS_OS_NAME" == "osx" ]];   then python3 -m pytest -p no:warnings  $TRAVIS_BUILD_DIR; fi
+- if [[ "$TRAVIS_OS_NAME" == "osx" ]];   then python -m pytest -p no:warnings  $TRAVIS_BUILD_DIR; fi
 
 before_install:
 - if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then docker rmi -f niedakh/scikit-multilearn-dev:latest    ; fi
+#- if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then docker rm -f scikit_multilearn_dev_test_docker    ; fi
 - if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then docker pull niedakh/scikit-multilearn-dev:latest      ; fi
 
 - if [[ "$TRAVIS_OS_NAME" == "osx" ]];   then pip2 uninstall -y numpy                               ; fi
@@ -37,20 +38,25 @@ before_install:
 - if [[ "$TRAVIS_OS_NAME" == "osx" ]];   then brew install graph-tool                               ; fi
 - if [[ "$TRAVIS_OS_NAME" == "osx" ]];   then mkdir ~/MEKA && wget https://adams.cms.waikato.ac.nz/snapshots/meka/meka-snapshot.zip -O ~/MEKA/meka.zip; fi
 - if [[ "$TRAVIS_OS_NAME" == "osx" ]];   then cd ~/MEKA/ && unzip -a meka.zip && mv meka-* meka; fi
+- if [[ "$TRAVIS_OS_NAME" == "osx" ]];   then git clone https://github.com/thunlp/OpenNE.git ~/OpenNE && pip3 install -e ~/OpenNE/src; fi
 
 install:
 - if [[ "$TRAVIS_OS_NAME" != "osx" ]];   then docker run -e "MEKA_CLASSPATH=/opt/meka/lib/" -v "$TRAVIS_BUILD_DIR:/home/python-dev/repo" --name scikit_multilearn_dev_test_docker -d niedakh/scikit-multilearn-dev:latest; fi
-- if [[ "$TOXENV" == "linux_py2" ]]; then docker exec -it scikit_multilearn_dev_test_docker pip install -r /home/python-dev/repo/requirements-all.txt; fi
-- if [[ "$TOXENV" == "linux_py3" ]]; then docker exec -it scikit_multilearn_dev_test_docker pip3 install -r /home/python-dev/repo/requirements-all.txt; fi
-- if [[ "$TOXENV" == "linux_py2" ]]; then docker exec -it scikit_multilearn_dev_test_docker pip install -r /home/python-dev/repo/requirements/keras.txt; fi
-- if [[ "$TOXENV" == "linux_py3" ]]; then docker exec -it scikit_multilearn_dev_test_docker pip3 install -r /home/python-dev/repo/requirements/keras.txt; fi
+- if [[ "$TOXENV" == "linux_py2" ]]; then docker exec -it scikit_multilearn_dev_test_docker pip2 install --upgrade --force-reinstall numpy scipy scikit-learn; fi
+- if [[ "$TOXENV" == "linux_py3" ]]; then docker exec -it scikit_multilearn_dev_test_docker pip3 install --upgrade --force-reinstall numpy scipy scikit-learn; fi
+#- if [[ "$TOXENV" == "linux_py2" ]]; then docker exec -it scikit_multilearn_dev_test_docker pip install -r /home/python-dev/repo/requirements/keras.txt; fi
+#- if [[ "$TOXENV" == "linux_py3" ]]; then docker exec -it scikit_multilearn_dev_test_docker pip3 install -r /home/python-dev/repo/requirements/keras.txt; fi
 - if [[ "$TRAVIS_OS_NAME" != "osx" ]];   then find $TRAVIS_BUILD_DIR -name \*.pyc -delete; fi
 
-- if [[ "$TRAVIS_OS_NAME" == "osx" ]];   then cd $TRAVIS_BUILD_DIR && pip install -r requirements-all.txt ; fi
 - if [[ "$TRAVIS_OS_NAME" == "osx" ]];   then cd $TRAVIS_BUILD_DIR && pip install -r requirements/keras.txt; fi
-- if [[ "$TRAVIS_OS_NAME" == "osx" ]];   then cd $TRAVIS_BUILD_DIR && pip3 install -r requirements-all.txt; fi
+- if [[ "$TRAVIS_OS_NAME" == "osx" ]];   then cd $TRAVIS_BUILD_DIR && pip install -r requirements-all.txt ; fi
+- if [[ "$TRAVIS_OS_NAME" == "osx" ]];   then cd $TRAVIS_BUILD_DIR && pip install --upgrade --force-reinstall numpy scipy scikit-learn; fi
+
 - if [[ "$TRAVIS_OS_NAME" == "osx" ]];   then cd $TRAVIS_BUILD_DIR && pip3 install https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.12.0-py3-none-any.whl; fi
 - if [[ "$TRAVIS_OS_NAME" == "osx" ]];   then cd $TRAVIS_BUILD_DIR && pip3 install -r requirements/keras.txt; fi
+- if [[ "$TRAVIS_OS_NAME" == "osx" ]];   then cd $TRAVIS_BUILD_DIR && pip3 install -r requirements-all.txt; fi
+- if [[ "$TRAVIS_OS_NAME" == "osx" ]];   then cd $TRAVIS_BUILD_DIR && pip3 install --upgrade --force-reinstall numpy scipy scikit-learn; fi
+
 
 notifications:
   slack:

appveyor.yml

@@ -25,21 +25,21 @@ environment:
 
     # Python 2.7.10 is the latest version and is not pre-installed.
 
-    - PYTHON: "C:\\Python27.10"
-      PYTHON_VERSION: "2.7.10"
+    - PYTHON: "C:\\Python27.14"
+      PYTHON_VERSION: "2.7.14"
       PYTHON_ARCH: "32"
       HAS_KERAS: "0"
 
-    - PYTHON: "C:\\Python27.10-x64"
-      PYTHON_VERSION: "2.7.10"
+    - PYTHON: "C:\\Python27.14-x64"
+      PYTHON_VERSION: "2.7.14"
       PYTHON_ARCH: "64"
       HAS_KERAS: "0"
 
     # Pre-installed Python versions, which Appveyor may upgrade to
     # a later point release.
     # See: http://www.appveyor.com/docs/installed-software#python
 
-    - PYTHON: "C:\\Python35"
+    - PYTHON: "C:\\Python36"
       PYTHON_VERSION: "3.6.x" # currently 3.4.3
       PYTHON_ARCH: "32"
       HAS_KERAS: "0"
@@ -81,30 +81,34 @@ install:
   - "SET PATH=%PYTHON%;%PYTHON%\\Scripts;%PATH%"
 
   # Check that we have the expected version and architecture for Python
-  - "python --version"
-  - "python -c \"import struct; print(struct.calcsize('P') * 8)\""
+  - "%PYTHON%\\python.exe --version"
+  - "%PYTHON%\\python.exe -c \"import struct; print(struct.calcsize('P') * 8)\""
 
   # Upgrade to the latest version of pip to avoid it displaying warnings
   # about it being out of date.
-  - "python -m pip install --upgrade pip"
+  - "%PYTHON%\\python.exe -m pip install --upgrade pip"
 
   # Install the build dependencies of the project. If some dependencies contain
   # compiled extensions and are not provided as pre-built wheel packages,
   # pip will build them from source using the MSVC compiler matching the
   # target Python version and architecture
-  - "pip install -r requirements\\base.txt"
-  - "pip install -r requirements\\meka.txt"
-  - ps: If ($env:HAS_KERAS -eq "1")  { & pip install -r requirements\\keras.txt }
+  - "%PYTHON%\\Scripts\\pip.exe install -r requirements\\base.txt"
+  - "%PYTHON%\\Scripts\\pip.exe install -r requirements\\meka.txt"
+  - ps: If ($env:HAS_KERAS -eq "1")  { &  $env:PYTHON\Scripts\pip.exe install -r requirements\keras.txt }
+  - git clone https://github.com/thunlp/OpenNE C:\projects\OpenNE
+  - ps: If ($env:HAS_KERAS -eq "1")  { &  $env:PYTHON\Scripts\pip.exe install -e C:/projects/OpenNE/src }
+  - "%PYTHON%\\Scripts\\pip.exe install --upgrade scikit-learn numpy scipy"
   # - "pip install -r requirements\\gpl.txt"
-  - "pip install -r requirements\\test.txt"
+  - "%PYTHON%\\Scripts\\pip.exe install -r requirements\\test.txt"
+  - "%PYTHON%\\Scripts\\pip.exe install --upgrade --force-reinstall numpy scipy scikit-learn"
 
 #build_script:
   # Build the compiled extension
   #- "%CMD_IN_ENV% python setup.py build"
 
 test_script:
   # Run the project tests
-  - "py.test skmultilearn"
+  - "%PYTHON%\\python.exe -m pytest -p no:warnings skmultilearn"
 
 #after_test:
   # If tests are successful, create binary packages for the project.

skmultilearn/embedding/__init__.py

@@ -0,0 +1,34 @@
+"""
+The :mod:`skmultilearn.embedding` module provides implementations of label space embedding methods and a general
+embedding based classifier.
+
+
++--------------------------------------------------------+---------------------------------------------------------------+
+| Name                                                   | Description                                                   |
++========================================================+===============================================================+
+| :class:`~skmultilearn.embedding.CLEMS`                 | Cost-Sensitive Label Embedding with Multidimensional Scaling  |
++--------------------------------------------------------+---------------------------------------------------------------+
+| :class:`~skmultilearn.embedding.OpenNetworkEmbedder`   | Label Network Embedding for Multilabel Classification         |
++--------------------------------------------------------+---------------------------------------------------------------+
+| :class:`~skmultilearn.embedding.SKLearnEmbedder`       | Wrapper for scikit-learn embedders                            |
++--------------------------------------------------------+---------------------------------------------------------------+
+| :class:`~skmultilearn.embedding.EmbeddingClassifier`   | A general embedding-based classifier                          |
++--------------------------------------------------------+---------------------------------------------------------------+
+
+"""
+
+from .clems import CLEMS
+from .skembedings import SKLearnEmbedder
+from .classifier import EmbeddingClassifier
+import sys, platform
+
+__all__ = [
+    'CLEMS',
+    'SKLearnEmbedder',
+    'EmbeddingClassifier'
+]
+
+if not (sys.version_info[0] == 2 or platform.architecture()[0] == '32bit'):
+    from .openne import OpenNetworkEmbedder
+
+    __all__.append('OpenNetworkEmbedder')