1
0
mirror of https://github.com/gsi-upm/senpy synced 2025-04-19 11:19:07 +00:00

Compare commits

..

No commits in common. "master" and "0.9.1" have entirely different histories.

193 changed files with 47534 additions and 24598 deletions

1
.gitignore vendored
View File

@ -8,4 +8,3 @@ __pycache__
VERSION
Dockerfile-*
Dockerfile
senpy_data

View File

@ -4,130 +4,107 @@
# - docker:dind
# When using dind, it's wise to use the overlayfs driver for
# improved performance.
stages:
- test
- publish
- test_image
- push
- deploy
- clean
variables:
KUBENS: senpy
LATEST_IMAGE: "${HUB_REPO}:${CI_COMMIT_SHORT_SHA}"
SENPY_DATA: "/senpy-data/" # This is configured in the CI job
NLTK_DATA: "/senpy-data/nltk_data" # Store NLTK downloaded data
before_script:
- make -e login
docker:
stage: publish
image:
name: gcr.io/kaniko-project/executor:debug
entrypoint: [""]
variables:
PYTHON_VERSION: "3.10"
tags:
- docker
script:
- echo $CI_COMMIT_TAG > senpy/VERSION
- sed "s/{{PYVERSION}}/$PYTHON_VERSION/" Dockerfile.template > Dockerfile
- echo "{\"auths\":{\"$CI_REGISTRY\":{\"username\":\"$CI_REGISTRY_USER\",\"password\":\"$CI_REGISTRY_PASSWORD\"},\"https://index.docker.io/v1/\":{\"auth\":\"$HUB_AUTH\"}}}" > /kaniko/.docker/config.json
# The skip-tls-verify flag is there because our registry certificate is self signed
- /kaniko/executor --context $CI_PROJECT_DIR --skip-tls-verify --dockerfile $CI_PROJECT_DIR/Dockerfile --destination $CI_REGISTRY_IMAGE:$CI_COMMIT_TAG --destination $HUB_REPO:$CI_COMMIT_TAG
only:
- tags
docker-latest:
stage: publish
image:
name: gcr.io/kaniko-project/executor:debug
entrypoint: [""]
variables:
PYTHON_VERSION: "3.10"
tags:
- docker
script:
- echo git.${CI_COMMIT_SHORT_SHA} > senpy/VERSION
- sed "s/{{PYVERSION}}/$PYTHON_VERSION/" Dockerfile.template > Dockerfile
- echo "{\"auths\":{\"$CI_REGISTRY\":{\"username\":\"$CI_REGISTRY_USER\",\"password\":\"$CI_REGISTRY_PASSWORD\"},\"https://index.docker.io/v1/\":{\"auth\":\"$HUB_AUTH\"}}}" > /kaniko/.docker/config.json
# The skip-tls-verify flag is there because our registry certificate is self signed
- /kaniko/executor --context $CI_PROJECT_DIR --skip-tls-verify --dockerfile $CI_PROJECT_DIR/Dockerfile --destination $LATEST_IMAGE --destination "${HUB_REPO}:latest"
only:
refs:
- master
testimage:
only:
- tags
tags:
- docker
stage: test_image
image: "$CI_REGISTRY_IMAGE:$CI_COMMIT_TAG"
script:
- python -m senpy --no-run --test
testpy37:
tags:
- docker
variables:
SENPY_STRICT: "false"
image: python:3.7
.test: &test_definition
stage: test
script:
- pip install -r requirements.txt -r test-requirements.txt
- python setup.py test
- make -e test-$PYTHON_VERSION
testpy310:
tags:
- docker
test-3.5:
<<: *test_definition
variables:
SENPY_STRICT: "true"
image: python:3.10
stage: test
script:
- pip install -r requirements.txt -r test-requirements.txt -r extra-requirements.txt
- python setup.py test
PYTHON_VERSION: "3.5"
push_pypi:
test-2.7:
<<: *test_definition
variables:
PYTHON_VERSION: "2.7"
.image: &image_definition
stage: push
script:
- make -e push-$PYTHON_VERSION
only:
- tags
tags:
- docker
image: python:3.10
stage: publish
script:
- echo $CI_COMMIT_TAG > senpy/VERSION
- pip install twine
- python setup.py sdist bdist_wheel
- TWINE_PASSWORD=$PYPI_PASSWORD TWINE_USERNAME=$PYPI_USERNAME python -m twine upload dist/*
- triggers
- fix-makefiles
check_pypi:
only:
- tags
tags:
- docker
image: python:3.10
stage: deploy
script:
- pip install senpy==$CI_COMMIT_TAG
# Allow PYPI to update its index before we try to install
when: delayed
start_in: 10 minutes
push-3.5:
<<: *image_definition
variables:
PYTHON_VERSION: "3.5"
latest-demo:
push-2.7:
<<: *image_definition
variables:
PYTHON_VERSION: "2.7"
push-latest:
<<: *image_definition
variables:
PYTHON_VERSION: latest
only:
refs:
- master
tags:
- docker
image: alpine/k8s:1.22.6
- triggers
push-github:
stage: deploy
environment: production
variables:
KUBECONFIG: "/kubeconfig"
# Same image as docker-latest
IMAGEWTAG: "${LATEST_IMAGE}"
KUBEAPP: "senpy"
script:
- echo "${KUBECONFIG_RAW}" > $KUBECONFIG
- kubectl --kubeconfig $KUBECONFIG version
- cd k8s/
- cat *.yaml *.tmpl 2>/dev/null | envsubst | kubectl --kubeconfig $KUBECONFIG apply --namespace ${KUBENS:-default} -f -
- kubectl --kubeconfig $KUBECONFIG get all,ing -l app=${KUBEAPP} --namespace=${KUBENS:-default}
- make -e push-github
only:
- master
- triggers
- fix-makefiles
deploy_pypi:
stage: deploy
script: # Configure the PyPI credentials, then push the package, and cleanup the creds.
- echo "[server-login]" >> ~/.pypirc
- echo "repository=https://upload.pypi.org/legacy/" >> ~/.pypirc
- echo "username=" ${PYPI_USER} >> ~/.pypirc
- echo "password=" ${PYPI_PASSWORD} >> ~/.pypirc
- make pip_upload
- echo "" > ~/.pypirc && rm ~/.pypirc # If the above fails, this won't run.
only:
- /^v?\d+\.\d+\.\d+([abc]\d*)?$/ # PEP-440 compliant version (tags)
except:
- branches
deploy:
stage: deploy
environment: test
script:
- make -e deploy
only:
- master
- fix-makefiles
push-github:
stage: deploy
script:
- make -e push-github
only:
- master
- triggers
clean :
stage: clean
script:
- make -e clean
when: manual
cleanup_py:
stage: clean
when: always # this is important; run even if preceding stages failed.
script:
- rm -vf ~/.pypirc # we don't want to leave these around, but GitLab may clean up anyway.
- docker logout

View File

@ -2,7 +2,7 @@ These makefiles are recipes for several common tasks in different types of proje
To add them to your project, simply do:
```
git remote add makefiles ssh://git@lab.gsi.upm.es:2200/docs/templates/makefiles.git
git remote add makefiles ssh://git@lab.cluster.gsi.dit.upm.es:2200/docs/templates/makefiles.git
git subtree add --prefix=.makefiles/ makefiles master
touch Makefile
echo "include .makefiles/base.mk" >> Makefile
@ -16,7 +16,7 @@ include .makefiles/python.mk
```
You may need to set special variables like the name of your project or the python versions you're targetting.
Take a look at each specific `.mk` file for more information, and the `Makefile` in the [senpy](https://lab.gsi.upm.es/senpy/senpy) project for a real use case.
Take a look at each specific `.mk` file for more information, and the `Makefile` in the [senpy](https://lab.cluster.gsi.dit.upm.es/senpy/senpy) project for a real use case.
If you update the makefiles from your repository, make sure to push the changes for review in upstream (this repository):

View File

@ -2,16 +2,18 @@ export
NAME ?= $(shell basename $(CURDIR))
VERSION ?= $(shell git describe --tags --dirty 2>/dev/null)
ifeq ($(VERSION),)
VERSION:=unknown
endif
# Get the location of this makefile.
MK_DIR := $(dir $(abspath $(lastword $(MAKEFILE_LIST))))
-include .env
-include ../.env
.FORCE:
version: .FORCE
@echo $(VERSION) > $(NAME)/VERSION
@echo $(VERSION)
help: ## Show this help.
@fgrep -h "##" $(MAKEFILE_LIST) | fgrep -v fgrep | sed -e 's/\\$$//' | sed -e 's/\(.*:\)[^#]*##\s*\(.*\)/\1\t\2/' | column -t -s " "
@ -33,4 +35,4 @@ include $(MK_DIR)/git.mk
info:: ## List all variables
env
.PHONY:: config help ci
.PHONY:: config help ci version .FORCE

View File

@ -1,14 +1,4 @@
ifndef IMAGENAME
ifdef CI_REGISTRY_IMAGE
IMAGENAME=$(CI_REGISTRY_IMAGE)
else
IMAGENAME=$(NAME)
endif
endif
IMAGEWTAG?=$(IMAGENAME):$(VERSION)
DOCKER_FLAGS?=$(-ti)
DOCKER_CMD?=
IMAGEWTAG ?= $(IMAGENAME):$(VERSION)
docker-login: ## Log in to the registry. It will only be used in the server, or when running a CI task locally (if CI_BUILD_TOKEN is set).
ifeq ($(CI_BUILD_TOKEN),)
@ -28,24 +18,8 @@ else
@docker logout
endif
docker-run: ## Build a generic docker image
docker run $(DOCKER_FLAGS) $(IMAGEWTAG) $(DOCKER_CMD)
docker-build: ## Build a generic docker image
docker build . -t $(IMAGEWTAG)
docker-push: docker-login ## Push a generic docker image
docker push $(IMAGEWTAG)
docker-latest-push: docker-login ## Push the latest image
docker tag $(IMAGEWTAG) $(IMAGENAME)
docker push $(IMAGENAME)
login:: docker-login
clean:: docker-clean
docker-info:
@echo IMAGEWTAG=${IMAGEWTAG}
.PHONY:: docker-login docker-clean login clean

View File

@ -14,7 +14,7 @@ push-github: ## Push the code to github. You need to set up GITHUB_DEPLOY_KEY
ifeq ($(GITHUB_DEPLOY_KEY),)
else
$(eval KEY_FILE := "$(shell mktemp)")
@printf '%b' '$(GITHUB_DEPLOY_KEY)' > $(KEY_FILE)
@echo "$(GITHUB_DEPLOY_KEY)" > $(KEY_FILE)
@git remote rm github-deploy || true
git remote add github-deploy $(GITHUB_REPO)
-@GIT_SSH_COMMAND="ssh -i $(KEY_FILE)" git fetch github-deploy $(CI_COMMIT_REF_NAME)
@ -22,4 +22,7 @@ else
rm $(KEY_FILE)
endif
.PHONY:: commit tag git-push git-pull push-github
push:: git-push
pull:: git-pull
.PHONY:: commit tag push git-push git-pull push-github

View File

@ -13,7 +13,7 @@
KUBE_CA_TEMP=false
ifndef KUBE_CA_PEM_FILE
KUBE_CA_PEM_FILE:=$$PWD/.ca.crt
CREATED:=$(shell printf '%b\n' '$(KUBE_CA_BUNDLE)' > $(KUBE_CA_PEM_FILE))
CREATED:=$(shell echo -e "$(KUBE_CA_BUNDLE)" > $(KUBE_CA_PEM_FILE))
endif
KUBE_TOKEN?=""
KUBE_NAMESPACE?=$(NAME)

View File

@ -1,15 +1,17 @@
makefiles-remote:
git ls-remote --exit-code makefiles 2> /dev/null || git remote add makefiles ssh://git@lab.gsi.upm.es:2200/docs/templates/makefiles.git
@git remote add makefiles ssh://git@lab.cluster.gsi.dit.upm.es:2200/docs/templates/makefiles.git 2>/dev/null || true
makefiles-commit: makefiles-remote
git add -f .makefiles
git commit -em "Updated makefiles from ${NAME}"
makefiles-push:
git fetch makefiles $(NAME)
git subtree push --prefix=.makefiles/ makefiles $(NAME)
makefiles-pull: makefiles-remote
git subtree pull --prefix=.makefiles/ makefiles master --squash
.PHONY:: makefiles-remote makefiles-commit makefiles-push makefiles-pull
pull:: makefiles-pull
push:: makefiles-push
.PHONY:: makefiles-remote makefiles-commit makefiles-push makefiles-pull pull push

View File

@ -1,17 +1,9 @@
PYVERSIONS ?= 3.5
PYVERSIONS ?= 2.7
PYMAIN ?= $(firstword $(PYVERSIONS))
TARNAME ?= $(NAME)-$(VERSION).tar.gz
VERSIONFILE ?= $(NAME)/VERSION
DEVPORT ?= 6000
.FORCE:
version: .FORCE
@echo $(VERSION) > $(VERSIONFILE)
@echo $(VERSION)
yapf: ## Format python code
yapf -i -r $(NAME)
yapf -i -r tests
@ -26,10 +18,9 @@ Dockerfile-%: Dockerfile.template ## Generate a specific dockerfile (e.g. Docke
quick_build: $(addprefix build-, $(PYMAIN))
build: $(addprefix build-, $(PYVERSIONS)) ## Build all images / python versions
docker tag $(IMAGEWTAG)-python$(PYMAIN) $(IMAGEWTAG)
build-%: version Dockerfile-% ## Build a specific version (e.g. build-2.7)
docker build --pull -t '$(IMAGEWTAG)-python$*' -f Dockerfile-$* .;
docker build -t '$(IMAGEWTAG)-python$*' --cache-from $(IMAGENAME):python$* -f Dockerfile-$* .;
dev-%: ## Launch a specific development environment using docker (e.g. dev-2.7)
@docker start $(NAME)-dev$* || (\
@ -43,10 +34,10 @@ dev: dev-$(PYMAIN) ## Launch a development environment using docker, using the d
quick_test: test-$(PYMAIN)
test-%: build-% ## Run setup.py from in an isolated container, built from the base image. (e.g. test-2.7)
test-%: ## Run setup.py from in an isolated container, built from the base image. (e.g. test-2.7)
# This speeds tests up because the image has most (if not all) of the dependencies already.
docker rm $(NAME)-test-$* || true
docker create -ti --name $(NAME)-test-$* --entrypoint="" -w /usr/src/app/ $(IMAGEWTAG)-python$* python setup.py test
docker create -ti --name $(NAME)-test-$* --entrypoint="" -w /usr/src/app/ $(IMAGENAME):python$* python setup.py test
docker cp . $(NAME)-test-$*:/usr/src/app
docker start -a $(NAME)-test-$*
@ -76,7 +67,7 @@ pip_upload: pip_test ## Upload package to pip
push-latest: $(addprefix push-latest-,$(PYVERSIONS)) ## Push the "latest" tag to dockerhub
docker tag '$(IMAGEWTAG)-python$(PYMAIN)' '$(IMAGEWTAG)'
docker tag '$(IMAGEWTAG)-python$(PYMAIN)' '$(IMAGENAME):latest'
docker tag '$(IMAGEWTAG)-python$(PYMAIN)' '$(IMAGENAME)'
docker push '$(IMAGENAME):latest'
docker push '$(IMAGEWTAG)'
@ -98,4 +89,4 @@ clean:: ## Clean older docker images and containers related to this project and
@docker ps -a | grep $(IMAGENAME) | awk '{ split($$2, vers, "-"); if(vers[0] != "${VERSION}"){ print $$1;}}' | xargs docker rm -v 2>/dev/null|| true
@docker images | grep $(IMAGENAME) | awk '{ split($$2, vers, "-"); if(vers[0] != "${VERSION}"){ print $$1":"$$2;}}' | xargs docker rmi 2>/dev/null|| true
.PHONY:: yapf dockerfiles Dockerfile-% quick_build build build-% dev-% quick-dev test quick_test push-latest push-latest-% push-% push version .FORCE
.PHONY:: yapf dockerfiles Dockerfile-% quick_build build build-% dev-% quick-dev test quick_test push-latest push-latest-% push-% push

View File

@ -1,22 +0,0 @@
# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
version: 2
# Set the OS, Python version and other tools you might need
build:
os: ubuntu-22.04
tools:
python: "3.10"
# Build documentation in the "docs/" directory with Sphinx
sphinx:
configuration: docs/conf.py
# formats:
# - pdf
# - epub
python:
install:
- requirements: docs/requirements.txt

View File

@ -1,43 +1,12 @@
sudo: required
matrix:
allow_failures:
# Windows is experimental in Travis.
# As of this writing, senpy installs but hangs on tests that use the flask test client (e.g. blueprints)
- os: windows
include:
- os: linux
language: python
python: 3.4
before_install:
- pip install --upgrade --force-reinstall pandas
- os: linux
language: python
python: 3.5
- os: linux
language: python
python: 3.6
- os: linux
language: python
python: 3.7
- os: osx
language: generic
addons:
homebrew:
# update: true
packages: python3
before_install:
- python3 -m pip install --upgrade virtualenv
- virtualenv -p python3 --system-site-packages "$HOME/venv"
- source "$HOME/venv/bin/activate"
- os: windows
language: bash
before_install:
- choco install -y python3
- python -m pip install --upgrade pip
env: PATH=/c/Python37:/c/Python37/Scripts:$PATH
# command to run tests
# 'python' points to Python 2.7 on macOS but points to Python 3.7 on Linux and Windows
# 'python3' is a 'command not found' error on Windows but 'py' works on Windows only
script:
- python3 setup.py test || python setup.py test
services:
- docker
language: python
env:
- PYV=2.7
- PYV=3.5
# run nosetests - Tests
script: make test-$PYV

View File

@ -1,81 +0,0 @@
# Changelog
All notable changes to this project will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
## [Unreleased]
### Added
* The code of many senpy community plugins have been included by default. However, additional files (e.g., licensed data) and/or installing additional dependencies may be necessary for some plugins. Read each plugin's documentation for more information.
* `--strict` flag, to fail and not start when a
* `optional` attribute in plugins. Optional plugins may fail to load or activate but the server will be started regardless, unless running in strict mode
* Option in shelf plugins to ignore pickling errors
### Removed
* `--only-install`, `--only-test` and `--only-list` flags were removed in favor of `--no-run` + `--install`/`--test`/`--dependencies`
### Changed
* data directory selection logic is slightly modified, and will choose one of the following (in this order): `data_folder` (argument), `$SENPY_DATA` or `$CWD`
## [1.0.6]
### Fixed
* Plugins now get activated for testing
## [1.0.1]
### Added
* License headers
* Description for PyPI (setup.py)
### Changed
* The evaluation tab shows datasets inline, and a tooltip shows the number of instances
* The docs should be clearer now
## [1.0.0]
### Fixed
* Restored hash changing function in `main.js`
## 0.20
### Added
* Objects can control the keys that will be used in `serialize`/`jsonld`/`as_dict` by specifying a list of keys in `terse_keys`.
e.g.
```python
>>> class MyModel(senpy.models.BaseModel):
... _terse_keys = ['visible']
... invisible = 5
... visible = 1
...
>>> m = MyModel(id='testing')
>>> m.jsonld()
{'invisible': 5, 'visible': 1, '@id': 'testing'}
>>> m.jsonld(verbose=False)
{'visible': 1}
```
* Configurable logging format.
* Added default terse keys for the most common classes (entry, sentiment, emotion...).
* Flag parameters (boolean) are set to true even when no value is added (e.g. `&verbose` is the same as `&verbose=true`).
* Plugin and parameter descriptions are now formatted with (showdown)[https://github.com/showdownjs/showdown].
* The web UI requests extra_parameters from the server. This is useful for pipelines. See #52
* First batch of semantic tests (using SPARQL)
* `Plugin.path()` method to get a file path from a relative path (using the senpy data folder)
### Changed
* `install_deps` now checks what requirements are already met before installing with pip.
* Help is now provided verbosely by default
* Other outputs are terse by default. This means some properties are now hidden unless verbose is set.
* `sentiments` and `emotions` are now `marl:hasOpinion` and `onyx:hasEmotionSet`, respectively.
* Nicer logging format
* Context aliases (e.g. `sentiments` and `emotions` properties) have been replaced with the original properties (e.g. `marl:hasOpinion` and `onyx:hasEmotionSet**), to use aliases, pass the `aliases** parameter.
* Several UI improvements
* Dedicated tab to show the list of plugins
* URLs in plugin descriptions are shown as links
* The format of the response is selected by clicking on a tab instead of selecting from a drop-down
* list of examples
* Bootstrap v4
* RandEmotion and RandSentiment are no longer included in the base set of plugins
* The `--plugin-folder` option can be used more than once, and every folder will be added to the app.
### Deprecated
### Removed
* Python 2.7 is no longer test or officially supported
### Fixed
* Plugin descriptions are now dedented when they are extracted from the docstring.
### Security

View File

@ -2,24 +2,21 @@ from python:{{PYVERSION}}
MAINTAINER J. Fernando Sánchez <jf.sanchez@upm.es>
RUN apt-get update && apt-get install -y \
libblas-dev liblapack-dev liblapacke-dev gfortran \
&& rm -rf /var/lib/apt/lists/*
RUN mkdir -p /cache/ /senpy-plugins /data/
RUN mkdir /cache/ /senpy-plugins /data/
VOLUME /data/
ENV PIP_CACHE_DIR=/cache/ SENPY_DATA=/data
WORKDIR /usr/src/app
COPY test-requirements.txt requirements.txt extra-requirements.txt /usr/src/app/
RUN pip install --no-cache-dir -r test-requirements.txt -r requirements.txt -r extra-requirements.txt
COPY . /usr/src/app/
RUN pip install --no-cache-dir --no-index --no-deps --editable .
ONBUILD COPY . /senpy-plugins/
ONBUILD RUN python -m senpy -i --no-run -f /senpy-plugins
ONBUILD RUN python -m senpy --only-install -f /senpy-plugins
ONBUILD WORKDIR /senpy-plugins/
WORKDIR /usr/src/app
COPY test-requirements.txt requirements.txt /usr/src/app/
RUN pip install --use-wheel -r test-requirements.txt -r requirements.txt
COPY . /usr/src/app/
RUN pip install --no-index --no-deps --editable .
ENTRYPOINT ["python", "-m", "senpy", "-f", "/senpy-plugins/", "--host", "0.0.0.0"]

View File

@ -1,8 +1,6 @@
include requirements.txt
include test-requirements.txt
include extra-requirements.txt
include README.rst
include LICENSE.txt
include senpy/VERSION
graft senpy/plugins
graft senpy/schemas

View File

@ -5,7 +5,7 @@ IMAGENAME=gsiupm/senpy
# The first version is the main one (used for quick builds)
# See .makefiles/python.mk for more info
PYVERSIONS ?= 3.10 3.7
PYVERSIONS=3.5 2.7
DEVPORT=5000

View File

@ -1 +1 @@
web: python -m senpy --host 0.0.0.0 --port $PORT
web: python -m senpy --host 0.0.0.0 --port $PORT --default-plugins

View File

@ -1,25 +1,18 @@
.. image:: img/header.png
:width: 100%
:target: http://senpy.gsi.upm.es
.. image:: https://readthedocs.org/projects/senpy/badge/?version=latest
:target: http://senpy.readthedocs.io/en/latest/
.. image:: https://badge.fury.io/py/senpy.svg
:target: https://badge.fury.io/py/senpy
.. image:: https://travis-ci.org/gsi-upm/senpy.svg
:target: https://github.com/gsi-upm/senpy/senpy/tree/master
.. image:: https://img.shields.io/pypi/l/requests.svg
:target: https://lab.gsi.upm.es/senpy/senpy/
:target: http://demos.gsi.dit.upm.es/senpy
.. image:: https://travis-ci.org/gsi-upm/senpy.svg?branch=master
:target: https://travis-ci.org/gsi-upm/senpy
Senpy lets you create sentiment analysis web services easily, fast and using a well known API.
As a bonus, Senpy services use semantic vocabularies (e.g. `NIF <http://persistence.uni-leipzig.org/nlp2rdf/>`_, `Marl <http://www.gsi.upm.es/ontologies/marl>`_, `Onyx <http://www.gsi.upm.es/ontologies/onyx>`_) and formats (turtle, JSON-LD, xml-rdf).
As a bonus, senpy services use semantic vocabularies (e.g. `NIF <http://persistence.uni-leipzig.org/nlp2rdf/>`_, `Marl <http://www.gsi.dit.upm.es/ontologies/marl>`_, `Onyx <http://www.gsi.dit.upm.es/ontologies/onyx>`_) and formats (turtle, JSON-LD, xml-rdf).
Have you ever wanted to turn your sentiment analysis algorithms into a service?
With Senpy, now you can.
With senpy, now you can.
It provides all the tools so you just have to worry about improving your algorithms:
`See it in action. <http://senpy.gsi.upm.es/>`_
`See it in action. <http://senpy.cluster.gsi.dit.upm.es/>`_
Installation
------------
@ -41,36 +34,20 @@ Alternatively, you can use the development version:
cd senpy
pip install --user .
If you want to install Senpy globally, use sudo instead of the ``--user`` flag.
If you want to install senpy globally, use sudo instead of the ``--user`` flag.
Docker Image
************
Build the image or use the pre-built one: ``docker run -ti -p 5000:5000 gsiupm/senpy``.
Build the image or use the pre-built one: ``docker run -ti -p 5000:5000 gsiupm/senpy --default-plugins``.
To add custom plugins, add a volume and tell Senpy where to find the plugins: ``docker run -ti -p 5000:5000 -v <PATH OF PLUGINS>:/plugins gsiupm/senpy -f /plugins``
To add custom plugins, add a volume and tell senpy where to find the plugins: ``docker run -ti -p 5000:5000 -v <PATH OF PLUGINS>:/plugins gsiupm/senpy --default-plugins -f /plugins``
Compatibility
-------------
Senpy should run on any major operating system.
Its code is pure Python, and the only limitations are imposed by its dependencies (e.g., nltk, pandas).
Currently, the CI/CD pipeline tests the code on:
* GNU/Linux with Python versions 3.7+ (3.10+ recommended for all plugins)
* MacOS and homebrew's python3
* Windows 10 and chocolatey's python3
The latest PyPI package is verified to install on Ubuntu, Debian and Arch Linux.
If you have trouble installing Senpy on your platform, see `Having problems?`_.
Developing
----------
Running/debugging
*****************
Developing/debugging
********************
This command will run the senpy container using the latest image available, mounting your current folder so you get your latest code:
.. code:: bash
@ -133,7 +110,7 @@ or, alternatively:
This will create a server with any modules found in the current path.
For more options, see the `--help` page.
Alternatively, you can use the modules included in Senpy to build your own application.
Alternatively, you can use the modules included in senpy to build your own application.
Deploying on Heroku
-------------------
@ -141,31 +118,13 @@ Use a free heroku instance to share your service with the world.
Just use the example Procfile in this repository, or build your own.
`DEMO on heroku <http://senpy.herokuapp.com>`_
For more information, check out the `documentation <http://senpy.readthedocs.org>`_.
------------------------------------------------------------------------------------
Python 2.x compatibility
------------------------
Keeping compatibility between python 2.7 and 3.x is not always easy, especially for a framework that deals both with text and web requests.
Hence, starting February 2019, this project will no longer make efforts to support python 2.7, which will reach its end of life in 2020.
Most of the functionality should still work, and the compatibility shims will remain for now, but we cannot make any guarantees at this point.
Instead, the maintainers will focus their efforts on keeping the codebase compatible across different Python 3.3+ versions, including upcoming ones.
We apologize for the inconvenience.
Having problems?
----------------
Please, file a new issue `on GitHub <https://github.com/gsi-upm/senpy/issues>`_ including enough details to reproduce the bug, including:
* Operating system
* Version of Senpy (or docker tag)
* Installed libraries
* Relevant logs
* A simple code example
Acknowledgement
---------------
This development has been partially funded by the European Union through the MixedEmotions Project (project number H2020 655632), as part of the `RIA ICT 15 Big data and Open Data Innovation and take-up` programme.

4
config.py Normal file
View File

@ -0,0 +1,4 @@
import os
SERVER_PORT = os.environ.get("SERVER_PORT", 5000)
DEBUG = os.environ.get("DEBUG", True)

View File

@ -1,10 +0,0 @@
version: '3'
services:
senpy:
image: "${IMAGENAME-gsiupm/senpy}:${VERSION-latest}"
entrypoint: ["/bin/bash"]
working_dir: "/senpy-plugins"
ports:
- 5000:5000
volumes:
- ".:/usr/src/app/"

View File

@ -1,9 +0,0 @@
version: '3'
services:
test:
image: "${IMAGENAME-gsiupm/senpy}:${VERSION-dev}"
entrypoint: ["py.test"]
volumes:
- ".:/usr/src/app/"
command:
[]

View File

@ -1,11 +0,0 @@
version: '3'
services:
senpy:
image: "${IMAGENAME-gsiupm/senpy}:${VERSION-dev}"
build:
context: .
dockerfile: Dockerfile${PYVERSION--2.7}
ports:
- 5001:5000
volumes:
- "./data:/data"

File diff suppressed because it is too large Load Diff

View File

@ -1,592 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Evaluating Services"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sentiment analysis plugins can also be evaluated on a series of pre-defined datasets.\n",
"This can be done in three ways: through the Web UI (playground), through the web API and programmatically.\n",
"\n",
"Regardless of the way you perform the evaluation, you will need to specify a plugin (service) that you want to evaluate, and a series of datasets on which it should be evaluated.\n",
"\n",
"to evaluate a plugin on a dataset, senpy use the plugin to predict the sentiment in each entry in the dataset.\n",
"These predictions are compared with the expected values to produce several metrics, such as: accuracy, precision and f1-score.\n",
"\n",
"**note**: the evaluation process might take long for plugins that use external services, such as `sentiment140`.\n",
"\n",
"**note**: plugins are assumed to be pre-trained and invariant. i.e., the prediction for an entry should "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Web UI (Playground)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The playground should contain a tab for Evaluation, where you can select any plugin that can be evaluated, and the set of datasets that you want to test the plugin on.\n",
"\n",
"For example, the image below shows the results of the `sentiment-vader` plugin on the `vader` and `sts` datasets:\n",
"\n",
"\n",
"![](eval_table.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Web API"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The api exposes an endpoint (`/evaluate`), which accents the plugin and the set of datasets on which it should be evaluated."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following code is not necessary, but it will display the results better:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here is a simple call using the requests library:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>.output_html .hll { background-color: #ffffcc }\n",
".output_html { background: #f8f8f8; }\n",
".output_html .c { color: #408080; font-style: italic } /* Comment */\n",
".output_html .err { border: 1px solid #FF0000 } /* Error */\n",
".output_html .k { color: #008000; font-weight: bold } /* Keyword */\n",
".output_html .o { color: #666666 } /* Operator */\n",
".output_html .ch { color: #408080; font-style: italic } /* Comment.Hashbang */\n",
".output_html .cm { color: #408080; font-style: italic } /* Comment.Multiline */\n",
".output_html .cp { color: #BC7A00 } /* Comment.Preproc */\n",
".output_html .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */\n",
".output_html .c1 { color: #408080; font-style: italic } /* Comment.Single */\n",
".output_html .cs { color: #408080; font-style: italic } /* Comment.Special */\n",
".output_html .gd { color: #A00000 } /* Generic.Deleted */\n",
".output_html .ge { font-style: italic } /* Generic.Emph */\n",
".output_html .gr { color: #FF0000 } /* Generic.Error */\n",
".output_html .gh { color: #000080; font-weight: bold } /* Generic.Heading */\n",
".output_html .gi { color: #00A000 } /* Generic.Inserted */\n",
".output_html .go { color: #888888 } /* Generic.Output */\n",
".output_html .gp { color: #000080; font-weight: bold } /* Generic.Prompt */\n",
".output_html .gs { font-weight: bold } /* Generic.Strong */\n",
".output_html .gu { color: #800080; font-weight: bold } /* Generic.Subheading */\n",
".output_html .gt { color: #0044DD } /* Generic.Traceback */\n",
".output_html .kc { color: #008000; font-weight: bold } /* Keyword.Constant */\n",
".output_html .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */\n",
".output_html .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */\n",
".output_html .kp { color: #008000 } /* Keyword.Pseudo */\n",
".output_html .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */\n",
".output_html .kt { color: #B00040 } /* Keyword.Type */\n",
".output_html .m { color: #666666 } /* Literal.Number */\n",
".output_html .s { color: #BA2121 } /* Literal.String */\n",
".output_html .na { color: #7D9029 } /* Name.Attribute */\n",
".output_html .nb { color: #008000 } /* Name.Builtin */\n",
".output_html .nc { color: #0000FF; font-weight: bold } /* Name.Class */\n",
".output_html .no { color: #880000 } /* Name.Constant */\n",
".output_html .nd { color: #AA22FF } /* Name.Decorator */\n",
".output_html .ni { color: #999999; font-weight: bold } /* Name.Entity */\n",
".output_html .ne { color: #D2413A; font-weight: bold } /* Name.Exception */\n",
".output_html .nf { color: #0000FF } /* Name.Function */\n",
".output_html .nl { color: #A0A000 } /* Name.Label */\n",
".output_html .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */\n",
".output_html .nt { color: #008000; font-weight: bold } /* Name.Tag */\n",
".output_html .nv { color: #19177C } /* Name.Variable */\n",
".output_html .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */\n",
".output_html .w { color: #bbbbbb } /* Text.Whitespace */\n",
".output_html .mb { color: #666666 } /* Literal.Number.Bin */\n",
".output_html .mf { color: #666666 } /* Literal.Number.Float */\n",
".output_html .mh { color: #666666 } /* Literal.Number.Hex */\n",
".output_html .mi { color: #666666 } /* Literal.Number.Integer */\n",
".output_html .mo { color: #666666 } /* Literal.Number.Oct */\n",
".output_html .sa { color: #BA2121 } /* Literal.String.Affix */\n",
".output_html .sb { color: #BA2121 } /* Literal.String.Backtick */\n",
".output_html .sc { color: #BA2121 } /* Literal.String.Char */\n",
".output_html .dl { color: #BA2121 } /* Literal.String.Delimiter */\n",
".output_html .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */\n",
".output_html .s2 { color: #BA2121 } /* Literal.String.Double */\n",
".output_html .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */\n",
".output_html .sh { color: #BA2121 } /* Literal.String.Heredoc */\n",
".output_html .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */\n",
".output_html .sx { color: #008000 } /* Literal.String.Other */\n",
".output_html .sr { color: #BB6688 } /* Literal.String.Regex */\n",
".output_html .s1 { color: #BA2121 } /* Literal.String.Single */\n",
".output_html .ss { color: #19177C } /* Literal.String.Symbol */\n",
".output_html .bp { color: #008000 } /* Name.Builtin.Pseudo */\n",
".output_html .fm { color: #0000FF } /* Name.Function.Magic */\n",
".output_html .vc { color: #19177C } /* Name.Variable.Class */\n",
".output_html .vg { color: #19177C } /* Name.Variable.Global */\n",
".output_html .vi { color: #19177C } /* Name.Variable.Instance */\n",
".output_html .vm { color: #19177C } /* Name.Variable.Magic */\n",
".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"p\">{</span>\n",
" <span class=\"nt\">&quot;@context&quot;</span><span class=\"p\">:</span> <span class=\"s2\">&quot;http://senpy.gsi.upm.es/api/contexts/YXBpL2V2YWx1YXRlLz9hbGdvPXNlbnRpbWVudC12YWRlciZkYXRhc2V0PXZhZGVyJTJDc3RzJm91dGZvcm1hdD1qc29uLWxkIw%3D%3D&quot;</span><span class=\"p\">,</span>\n",
" <span class=\"nt\">&quot;@type&quot;</span><span class=\"p\">:</span> <span class=\"s2\">&quot;AggregatedEvaluation&quot;</span><span class=\"p\">,</span>\n",
" <span class=\"nt\">&quot;senpy:evaluations&quot;</span><span class=\"p\">:</span> <span class=\"p\">[</span>\n",
" <span class=\"p\">{</span>\n",
" <span class=\"nt\">&quot;@type&quot;</span><span class=\"p\">:</span> <span class=\"s2\">&quot;Evaluation&quot;</span><span class=\"p\">,</span>\n",
" <span class=\"nt\">&quot;evaluates&quot;</span><span class=\"p\">:</span> <span class=\"s2\">&quot;endpoint:plugins/sentiment-vader_0.1.1__vader&quot;</span><span class=\"p\">,</span>\n",
" <span class=\"nt\">&quot;evaluatesOn&quot;</span><span class=\"p\">:</span> <span class=\"s2\">&quot;vader&quot;</span><span class=\"p\">,</span>\n",
" <span class=\"nt\">&quot;metrics&quot;</span><span class=\"p\">:</span> <span class=\"p\">[</span>\n",
" <span class=\"p\">{</span>\n",
" <span class=\"nt\">&quot;@type&quot;</span><span class=\"p\">:</span> <span class=\"s2\">&quot;Accuracy&quot;</span><span class=\"p\">,</span>\n",
" <span class=\"nt\">&quot;value&quot;</span><span class=\"p\">:</span> <span class=\"mf\">0.6907142857142857</span>\n",
" <span class=\"p\">},</span>\n",
" <span class=\"p\">{</span>\n",
" <span class=\"nt\">&quot;@type&quot;</span><span class=\"p\">:</span> <span class=\"s2\">&quot;Precision_macro&quot;</span><span class=\"p\">,</span>\n",
" <span class=\"nt\">&quot;value&quot;</span><span class=\"p\">:</span> <span class=\"mf\">0.34535714285714286</span>\n",
" <span class=\"p\">},</span>\n",
" <span class=\"p\">{</span>\n",
" <span class=\"nt\">&quot;@type&quot;</span><span class=\"p\">:</span> <span class=\"s2\">&quot;Recall_macro&quot;</span><span class=\"p\">,</span>\n",
" <span class=\"nt\">&quot;value&quot;</span><span class=\"p\">:</span> <span class=\"mf\">0.5</span>\n",
" <span class=\"p\">},</span>\n",
" <span class=\"p\">{</span>\n",
" <span class=\"nt\">&quot;@type&quot;</span><span class=\"p\">:</span> <span class=\"s2\">&quot;F1_macro&quot;</span><span class=\"p\">,</span>\n",
" <span class=\"nt\">&quot;value&quot;</span><span class=\"p\">:</span> <span class=\"mf\">0.40853400929446554</span>\n",
" <span class=\"p\">},</span>\n",
" <span class=\"p\">{</span>\n",
" <span class=\"nt\">&quot;@type&quot;</span><span class=\"p\">:</span> <span class=\"s2\">&quot;F1_weighted&quot;</span><span class=\"p\">,</span>\n",
" <span class=\"nt\">&quot;value&quot;</span><span class=\"p\">:</span> <span class=\"mf\">0.5643605528396403</span>\n",
" <span class=\"p\">},</span>\n",
" <span class=\"p\">{</span>\n",
" <span class=\"nt\">&quot;@type&quot;</span><span class=\"p\">:</span> <span class=\"s2\">&quot;F1_micro&quot;</span><span class=\"p\">,</span>\n",
" <span class=\"nt\">&quot;value&quot;</span><span class=\"p\">:</span> <span class=\"mf\">0.6907142857142857</span>\n",
" <span class=\"p\">},</span>\n",
" <span class=\"p\">{</span>\n",
" <span class=\"nt\">&quot;@type&quot;</span><span class=\"p\">:</span> <span class=\"s2\">&quot;F1_macro&quot;</span><span class=\"p\">,</span>\n",
" <span class=\"nt\">&quot;value&quot;</span><span class=\"p\">:</span> <span class=\"mf\">0.40853400929446554</span>\n",
" <span class=\"p\">}</span>\n",
" <span class=\"p\">]</span>\n",
" <span class=\"p\">},</span>\n",
" <span class=\"p\">{</span>\n",
" <span class=\"nt\">&quot;@type&quot;</span><span class=\"p\">:</span> <span class=\"s2\">&quot;Evaluation&quot;</span><span class=\"p\">,</span>\n",
" <span class=\"nt\">&quot;evaluates&quot;</span><span class=\"p\">:</span> <span class=\"s2\">&quot;endpoint:plugins/sentiment-vader_0.1.1__sts&quot;</span><span class=\"p\">,</span>\n",
" <span class=\"nt\">&quot;evaluatesOn&quot;</span><span class=\"p\">:</span> <span class=\"s2\">&quot;sts&quot;</span><span class=\"p\">,</span>\n",
" <span class=\"nt\">&quot;metrics&quot;</span><span class=\"p\">:</span> <span class=\"p\">[</span>\n",
" <span class=\"p\">{</span>\n",
" <span class=\"nt\">&quot;@type&quot;</span><span class=\"p\">:</span> <span class=\"s2\">&quot;Accuracy&quot;</span><span class=\"p\">,</span>\n",
" <span class=\"nt\">&quot;value&quot;</span><span class=\"p\">:</span> <span class=\"mf\">0.3107177974434612</span>\n",
" <span class=\"p\">},</span>\n",
" <span class=\"p\">{</span>\n",
" <span class=\"nt\">&quot;@type&quot;</span><span class=\"p\">:</span> <span class=\"s2\">&quot;Precision_macro&quot;</span><span class=\"p\">,</span>\n",
" <span class=\"nt\">&quot;value&quot;</span><span class=\"p\">:</span> <span class=\"mf\">0.1553588987217306</span>\n",
" <span class=\"p\">},</span>\n",
" <span class=\"p\">{</span>\n",
" <span class=\"nt\">&quot;@type&quot;</span><span class=\"p\">:</span> <span class=\"s2\">&quot;Recall_macro&quot;</span><span class=\"p\">,</span>\n",
" <span class=\"nt\">&quot;value&quot;</span><span class=\"p\">:</span> <span class=\"mf\">0.5</span>\n",
" <span class=\"p\">},</span>\n",
" <span class=\"p\">{</span>\n",
" <span class=\"nt\">&quot;@type&quot;</span><span class=\"p\">:</span> <span class=\"s2\">&quot;F1_macro&quot;</span><span class=\"p\">,</span>\n",
" <span class=\"nt\">&quot;value&quot;</span><span class=\"p\">:</span> <span class=\"mf\">0.23705926481620407</span>\n",
" <span class=\"p\">},</span>\n",
" <span class=\"p\">{</span>\n",
" <span class=\"nt\">&quot;@type&quot;</span><span class=\"p\">:</span> <span class=\"s2\">&quot;F1_weighted&quot;</span><span class=\"p\">,</span>\n",
" <span class=\"nt\">&quot;value&quot;</span><span class=\"p\">:</span> <span class=\"mf\">0.14731706525451424</span>\n",
" <span class=\"p\">},</span>\n",
" <span class=\"p\">{</span>\n",
" <span class=\"nt\">&quot;@type&quot;</span><span class=\"p\">:</span> <span class=\"s2\">&quot;F1_micro&quot;</span><span class=\"p\">,</span>\n",
" <span class=\"nt\">&quot;value&quot;</span><span class=\"p\">:</span> <span class=\"mf\">0.3107177974434612</span>\n",
" <span class=\"p\">},</span>\n",
" <span class=\"p\">{</span>\n",
" <span class=\"nt\">&quot;@type&quot;</span><span class=\"p\">:</span> <span class=\"s2\">&quot;F1_macro&quot;</span><span class=\"p\">,</span>\n",
" <span class=\"nt\">&quot;value&quot;</span><span class=\"p\">:</span> <span class=\"mf\">0.23705926481620407</span>\n",
" <span class=\"p\">}</span>\n",
" <span class=\"p\">]</span>\n",
" <span class=\"p\">}</span>\n",
" <span class=\"p\">]</span>\n",
"<span class=\"p\">}</span>\n",
"</pre></div>\n"
],
"text/latex": [
"\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
"\\PY{p}{\\PYZob{}}\n",
" \\PY{n+nt}{\\PYZdq{}@context\\PYZdq{}}\\PY{p}{:} \\PY{l+s+s2}{\\PYZdq{}http://senpy.gsi.upm.es/api/contexts/YXBpL2V2YWx1YXRlLz9hbGdvPXNlbnRpbWVudC12YWRlciZkYXRhc2V0PXZhZGVyJTJDc3RzJm91dGZvcm1hdD1qc29uLWxkIw\\PYZpc{}3D\\PYZpc{}3D\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n+nt}{\\PYZdq{}@type\\PYZdq{}}\\PY{p}{:} \\PY{l+s+s2}{\\PYZdq{}AggregatedEvaluation\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n+nt}{\\PYZdq{}senpy:evaluations\\PYZdq{}}\\PY{p}{:} \\PY{p}{[}\n",
" \\PY{p}{\\PYZob{}}\n",
" \\PY{n+nt}{\\PYZdq{}@type\\PYZdq{}}\\PY{p}{:} \\PY{l+s+s2}{\\PYZdq{}Evaluation\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n+nt}{\\PYZdq{}evaluates\\PYZdq{}}\\PY{p}{:} \\PY{l+s+s2}{\\PYZdq{}endpoint:plugins/sentiment\\PYZhy{}vader\\PYZus{}0.1.1\\PYZus{}\\PYZus{}vader\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n+nt}{\\PYZdq{}evaluatesOn\\PYZdq{}}\\PY{p}{:} \\PY{l+s+s2}{\\PYZdq{}vader\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n+nt}{\\PYZdq{}metrics\\PYZdq{}}\\PY{p}{:} \\PY{p}{[}\n",
" \\PY{p}{\\PYZob{}}\n",
" \\PY{n+nt}{\\PYZdq{}@type\\PYZdq{}}\\PY{p}{:} \\PY{l+s+s2}{\\PYZdq{}Accuracy\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n+nt}{\\PYZdq{}value\\PYZdq{}}\\PY{p}{:} \\PY{l+m+mf}{0.6907142857142857}\n",
" \\PY{p}{\\PYZcb{}}\\PY{p}{,}\n",
" \\PY{p}{\\PYZob{}}\n",
" \\PY{n+nt}{\\PYZdq{}@type\\PYZdq{}}\\PY{p}{:} \\PY{l+s+s2}{\\PYZdq{}Precision\\PYZus{}macro\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n+nt}{\\PYZdq{}value\\PYZdq{}}\\PY{p}{:} \\PY{l+m+mf}{0.34535714285714286}\n",
" \\PY{p}{\\PYZcb{}}\\PY{p}{,}\n",
" \\PY{p}{\\PYZob{}}\n",
" \\PY{n+nt}{\\PYZdq{}@type\\PYZdq{}}\\PY{p}{:} \\PY{l+s+s2}{\\PYZdq{}Recall\\PYZus{}macro\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n+nt}{\\PYZdq{}value\\PYZdq{}}\\PY{p}{:} \\PY{l+m+mf}{0.5}\n",
" \\PY{p}{\\PYZcb{}}\\PY{p}{,}\n",
" \\PY{p}{\\PYZob{}}\n",
" \\PY{n+nt}{\\PYZdq{}@type\\PYZdq{}}\\PY{p}{:} \\PY{l+s+s2}{\\PYZdq{}F1\\PYZus{}macro\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n+nt}{\\PYZdq{}value\\PYZdq{}}\\PY{p}{:} \\PY{l+m+mf}{0.40853400929446554}\n",
" \\PY{p}{\\PYZcb{}}\\PY{p}{,}\n",
" \\PY{p}{\\PYZob{}}\n",
" \\PY{n+nt}{\\PYZdq{}@type\\PYZdq{}}\\PY{p}{:} \\PY{l+s+s2}{\\PYZdq{}F1\\PYZus{}weighted\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n+nt}{\\PYZdq{}value\\PYZdq{}}\\PY{p}{:} \\PY{l+m+mf}{0.5643605528396403}\n",
" \\PY{p}{\\PYZcb{}}\\PY{p}{,}\n",
" \\PY{p}{\\PYZob{}}\n",
" \\PY{n+nt}{\\PYZdq{}@type\\PYZdq{}}\\PY{p}{:} \\PY{l+s+s2}{\\PYZdq{}F1\\PYZus{}micro\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n+nt}{\\PYZdq{}value\\PYZdq{}}\\PY{p}{:} \\PY{l+m+mf}{0.6907142857142857}\n",
" \\PY{p}{\\PYZcb{}}\\PY{p}{,}\n",
" \\PY{p}{\\PYZob{}}\n",
" \\PY{n+nt}{\\PYZdq{}@type\\PYZdq{}}\\PY{p}{:} \\PY{l+s+s2}{\\PYZdq{}F1\\PYZus{}macro\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n+nt}{\\PYZdq{}value\\PYZdq{}}\\PY{p}{:} \\PY{l+m+mf}{0.40853400929446554}\n",
" \\PY{p}{\\PYZcb{}}\n",
" \\PY{p}{]}\n",
" \\PY{p}{\\PYZcb{}}\\PY{p}{,}\n",
" \\PY{p}{\\PYZob{}}\n",
" \\PY{n+nt}{\\PYZdq{}@type\\PYZdq{}}\\PY{p}{:} \\PY{l+s+s2}{\\PYZdq{}Evaluation\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n+nt}{\\PYZdq{}evaluates\\PYZdq{}}\\PY{p}{:} \\PY{l+s+s2}{\\PYZdq{}endpoint:plugins/sentiment\\PYZhy{}vader\\PYZus{}0.1.1\\PYZus{}\\PYZus{}sts\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n+nt}{\\PYZdq{}evaluatesOn\\PYZdq{}}\\PY{p}{:} \\PY{l+s+s2}{\\PYZdq{}sts\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n+nt}{\\PYZdq{}metrics\\PYZdq{}}\\PY{p}{:} \\PY{p}{[}\n",
" \\PY{p}{\\PYZob{}}\n",
" \\PY{n+nt}{\\PYZdq{}@type\\PYZdq{}}\\PY{p}{:} \\PY{l+s+s2}{\\PYZdq{}Accuracy\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n+nt}{\\PYZdq{}value\\PYZdq{}}\\PY{p}{:} \\PY{l+m+mf}{0.3107177974434612}\n",
" \\PY{p}{\\PYZcb{}}\\PY{p}{,}\n",
" \\PY{p}{\\PYZob{}}\n",
" \\PY{n+nt}{\\PYZdq{}@type\\PYZdq{}}\\PY{p}{:} \\PY{l+s+s2}{\\PYZdq{}Precision\\PYZus{}macro\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n+nt}{\\PYZdq{}value\\PYZdq{}}\\PY{p}{:} \\PY{l+m+mf}{0.1553588987217306}\n",
" \\PY{p}{\\PYZcb{}}\\PY{p}{,}\n",
" \\PY{p}{\\PYZob{}}\n",
" \\PY{n+nt}{\\PYZdq{}@type\\PYZdq{}}\\PY{p}{:} \\PY{l+s+s2}{\\PYZdq{}Recall\\PYZus{}macro\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n+nt}{\\PYZdq{}value\\PYZdq{}}\\PY{p}{:} \\PY{l+m+mf}{0.5}\n",
" \\PY{p}{\\PYZcb{}}\\PY{p}{,}\n",
" \\PY{p}{\\PYZob{}}\n",
" \\PY{n+nt}{\\PYZdq{}@type\\PYZdq{}}\\PY{p}{:} \\PY{l+s+s2}{\\PYZdq{}F1\\PYZus{}macro\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n+nt}{\\PYZdq{}value\\PYZdq{}}\\PY{p}{:} \\PY{l+m+mf}{0.23705926481620407}\n",
" \\PY{p}{\\PYZcb{}}\\PY{p}{,}\n",
" \\PY{p}{\\PYZob{}}\n",
" \\PY{n+nt}{\\PYZdq{}@type\\PYZdq{}}\\PY{p}{:} \\PY{l+s+s2}{\\PYZdq{}F1\\PYZus{}weighted\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n+nt}{\\PYZdq{}value\\PYZdq{}}\\PY{p}{:} \\PY{l+m+mf}{0.14731706525451424}\n",
" \\PY{p}{\\PYZcb{}}\\PY{p}{,}\n",
" \\PY{p}{\\PYZob{}}\n",
" \\PY{n+nt}{\\PYZdq{}@type\\PYZdq{}}\\PY{p}{:} \\PY{l+s+s2}{\\PYZdq{}F1\\PYZus{}micro\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n+nt}{\\PYZdq{}value\\PYZdq{}}\\PY{p}{:} \\PY{l+m+mf}{0.3107177974434612}\n",
" \\PY{p}{\\PYZcb{}}\\PY{p}{,}\n",
" \\PY{p}{\\PYZob{}}\n",
" \\PY{n+nt}{\\PYZdq{}@type\\PYZdq{}}\\PY{p}{:} \\PY{l+s+s2}{\\PYZdq{}F1\\PYZus{}macro\\PYZdq{}}\\PY{p}{,}\n",
" \\PY{n+nt}{\\PYZdq{}value\\PYZdq{}}\\PY{p}{:} \\PY{l+m+mf}{0.23705926481620407}\n",
" \\PY{p}{\\PYZcb{}}\n",
" \\PY{p}{]}\n",
" \\PY{p}{\\PYZcb{}}\n",
" \\PY{p}{]}\n",
"\\PY{p}{\\PYZcb{}}\n",
"\\end{Verbatim}\n"
],
"text/plain": [
"{\n",
" \"@context\": \"http://senpy.gsi.upm.es/api/contexts/YXBpL2V2YWx1YXRlLz9hbGdvPXNlbnRpbWVudC12YWRlciZkYXRhc2V0PXZhZGVyJTJDc3RzJm91dGZvcm1hdD1qc29uLWxkIw%3D%3D\",\n",
" \"@type\": \"AggregatedEvaluation\",\n",
" \"senpy:evaluations\": [\n",
" {\n",
" \"@type\": \"Evaluation\",\n",
" \"evaluates\": \"endpoint:plugins/sentiment-vader_0.1.1__vader\",\n",
" \"evaluatesOn\": \"vader\",\n",
" \"metrics\": [\n",
" {\n",
" \"@type\": \"Accuracy\",\n",
" \"value\": 0.6907142857142857\n",
" },\n",
" {\n",
" \"@type\": \"Precision_macro\",\n",
" \"value\": 0.34535714285714286\n",
" },\n",
" {\n",
" \"@type\": \"Recall_macro\",\n",
" \"value\": 0.5\n",
" },\n",
" {\n",
" \"@type\": \"F1_macro\",\n",
" \"value\": 0.40853400929446554\n",
" },\n",
" {\n",
" \"@type\": \"F1_weighted\",\n",
" \"value\": 0.5643605528396403\n",
" },\n",
" {\n",
" \"@type\": \"F1_micro\",\n",
" \"value\": 0.6907142857142857\n",
" },\n",
" {\n",
" \"@type\": \"F1_macro\",\n",
" \"value\": 0.40853400929446554\n",
" }\n",
" ]\n",
" },\n",
" {\n",
" \"@type\": \"Evaluation\",\n",
" \"evaluates\": \"endpoint:plugins/sentiment-vader_0.1.1__sts\",\n",
" \"evaluatesOn\": \"sts\",\n",
" \"metrics\": [\n",
" {\n",
" \"@type\": \"Accuracy\",\n",
" \"value\": 0.3107177974434612\n",
" },\n",
" {\n",
" \"@type\": \"Precision_macro\",\n",
" \"value\": 0.1553588987217306\n",
" },\n",
" {\n",
" \"@type\": \"Recall_macro\",\n",
" \"value\": 0.5\n",
" },\n",
" {\n",
" \"@type\": \"F1_macro\",\n",
" \"value\": 0.23705926481620407\n",
" },\n",
" {\n",
" \"@type\": \"F1_weighted\",\n",
" \"value\": 0.14731706525451424\n",
" },\n",
" {\n",
" \"@type\": \"F1_micro\",\n",
" \"value\": 0.3107177974434612\n",
" },\n",
" {\n",
" \"@type\": \"F1_macro\",\n",
" \"value\": 0.23705926481620407\n",
" }\n",
" ]\n",
" }\n",
" ]\n",
"}"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import requests\n",
"from IPython.display import Code\n",
"\n",
"endpoint = 'http://senpy.gsi.upm.es/api'\n",
"res = requests.get(f'{endpoint}/evaluate',\n",
" params={\"algo\": \"sentiment-vader\",\n",
" \"dataset\": \"vader,sts\",\n",
" 'outformat': 'json-ld'\n",
" })\n",
"Code(res.text, language='json')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Programmatically (expert)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A third option is to evaluate plugins manually without launching the server.\n",
"\n",
"This option is particularly interesting for advanced users that want faster iterations and evaluation results, and for automation.\n",
"\n",
"We would first need an instance of a plugin.\n",
"In this example we will use the Sentiment140 plugin that is included in every senpy installation:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"from senpy.plugins.sentiment import sentiment140_plugin"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"s140 = sentiment140_plugin.Sentiment140()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, we need to know what datasets are available.\n",
"We can list all datasets and basic stats (e.g., number of instances and labels used) like this:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"vader {'instances': 4200, 'labels': [1, -1]}\n",
"sts {'instances': 4200, 'labels': [1, -1]}\n",
"imdb_unsup {'instances': 50000, 'labels': [1, -1]}\n",
"imdb {'instances': 50000, 'labels': [1, -1]}\n",
"sst {'instances': 11855, 'labels': [1, -1]}\n",
"multidomain {'instances': 38548, 'labels': [1, -1]}\n",
"sentiment140 {'instances': 1600000, 'labels': [1, -1]}\n",
"semeval07 {'instances': 'None', 'labels': [1, -1]}\n",
"semeval14 {'instances': 7838, 'labels': [1, -1]}\n",
"pl04 {'instances': 4000, 'labels': [1, -1]}\n",
"pl05 {'instances': 10662, 'labels': [1, -1]}\n",
"semeval13 {'instances': 6259, 'labels': [1, -1]}\n"
]
}
],
"source": [
"from senpy.gsitk_compat import datasets\n",
"for k, d in datasets.items():\n",
" print(k, d['stats'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we will evaluate our plugin in one of the smallest datasets, `sts`:"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"[{\n",
" \"@type\": \"Evaluation\",\n",
" \"evaluates\": \"endpoint:plugins/sentiment140_0.2\",\n",
" \"evaluatesOn\": \"sts\",\n",
" \"metrics\": [\n",
" {\n",
" \"@type\": \"Accuracy\",\n",
" \"value\": 0.872173058013766\n",
" },\n",
" {\n",
" \"@type\": \"Precision_macro\",\n",
" \"value\": 0.9035254323131467\n",
" },\n",
" {\n",
" \"@type\": \"Recall_macro\",\n",
" \"value\": 0.8021249029415483\n",
" },\n",
" {\n",
" \"@type\": \"F1_macro\",\n",
" \"value\": 0.8320673712021136\n",
" },\n",
" {\n",
" \"@type\": \"F1_weighted\",\n",
" \"value\": 0.8631351567604358\n",
" },\n",
" {\n",
" \"@type\": \"F1_micro\",\n",
" \"value\": 0.872173058013766\n",
" },\n",
" {\n",
" \"@type\": \"F1_macro\",\n",
" \"value\": 0.8320673712021136\n",
" }\n",
" ]\n",
" }]"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s140.evaluate(['sts', ])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
},
"toc": {
"colors": {
"hover_highlight": "#DAA520",
"running_highlight": "#FF0000",
"selected_highlight": "#FFD700"
},
"moveMenuLeft": true,
"nav_menu": {
"height": "68px",
"width": "252px"
},
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"threshold": 4,
"toc_cell": false,
"toc_section_display": "block",
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 1
}

View File

@ -24,7 +24,6 @@ I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
help:
@echo "Please use \`make <target>' where <target> is one of"
@echo " html to make standalone HTML files"
@echo " entr to watch for changes and continuously make HTML files"
@echo " dirhtml to make HTML files named index.html in directories"
@echo " singlehtml to make a single large HTML file"
@echo " pickle to make pickle files"
@ -50,9 +49,6 @@ help:
clean:
rm -rf $(BUILDDIR)/*
entr:
while true; do ag -g rst | entr -d make html; done
html:
$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
@echo

File diff suppressed because it is too large Load Diff

317
docs/SenpyClientUse.ipynb Normal file
View File

@ -0,0 +1,317 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-04-10T17:05:31.465571Z",
"start_time": "2017-04-10T19:05:31.458282+02:00"
},
"deletable": true,
"editable": true
},
"source": [
"# Client"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"The built-in senpy client allows you to query any Senpy endpoint. We will illustrate how to use it with the public demo endpoint, and then show you how to spin up your own endpoint using docker."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Demo Endpoint\n",
"-------------"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"To start using senpy, simply create a new Client and point it to your endpoint. In this case, the latest version of Senpy at GSI."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2017-04-10T17:29:12.827640Z",
"start_time": "2017-04-10T19:29:12.818617+02:00"
},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from senpy.client import Client\n",
"\n",
"c = Client('http://latest.senpy.cluster.gsi.dit.upm.es/api')\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Now, let's use that client analyse some queries:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2017-04-10T17:29:14.011657Z",
"start_time": "2017-04-10T19:29:13.701808+02:00"
},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"r = c.analyse('I like sugar!!', algorithm='sentiment140')\n",
"r"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-04-10T17:08:19.616754Z",
"start_time": "2017-04-10T19:08:19.610767+02:00"
},
"deletable": true,
"editable": true
},
"source": [
"As you can see, that gave us the full JSON result. A more concise way to print it would be:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2017-04-10T17:29:14.854213Z",
"start_time": "2017-04-10T19:29:14.842068+02:00"
},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"for entry in r.entries:\n",
" print('{} -> {}'.format(entry['text'], entry['sentiments'][0]['marl:hasPolarity']))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"We can also obtain a list of available plugins with the client:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2017-04-10T17:29:16.245198Z",
"start_time": "2017-04-10T19:29:16.056545+02:00"
},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"c.plugins()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Or, more concisely:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2017-04-10T17:29:17.663275Z",
"start_time": "2017-04-10T19:29:17.484623+02:00"
},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"c.plugins().keys()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Local Endpoint\n",
"--------------"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"To run your own instance of senpy, just create a docker container with the latest Senpy image. Using `--default-plugins` you will get some extra plugins to start playing with the API."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2017-04-10T17:29:20.637539Z",
"start_time": "2017-04-10T19:29:19.938322+02:00"
},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"!docker run -ti --name 'SenpyEndpoint' -d -p 6000:5000 gsiupm/senpy:0.8.6 --host 0.0.0.0 --default-plugins"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"To use this endpoint:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2017-04-10T17:29:21.263976Z",
"start_time": "2017-04-10T19:29:21.260595+02:00"
},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"c_local = Client('http://127.0.0.1:6000/api')"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"That's all! After you are done with your analysis, stop the docker container:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2017-04-10T17:29:33.226686Z",
"start_time": "2017-04-10T19:29:22.392121+02:00"
},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"!docker stop SenpyEndpoint\n",
"!docker rm SenpyEndpoint"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
},
"toc": {
"colors": {
"hover_highlight": "#DAA520",
"running_highlight": "#FF0000",
"selected_highlight": "#FFD700"
},
"moveMenuLeft": true,
"nav_menu": {
"height": "68px",
"width": "252px"
},
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"threshold": 4,
"toc_cell": false,
"toc_section_display": "block",
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 1
}

11
docs/about.rst Normal file
View File

@ -0,0 +1,11 @@
About
--------
If you use Senpy in your research, please cite `Senpy: A Pragmatic Linked Sentiment Analysis Framework <http://gsi.dit.upm.es/index.php/es/investigacion/publicaciones?view=publication&task=show&id=417>`__ (`BibTex <http://gsi.dit.upm.es/index.php/es/investigacion/publicaciones?controller=publications&task=export&format=bibtex&id=417>`__):
.. code-block:: text
Sánchez-Rada, J. F., Iglesias, C. A., Corcuera, I., & Araque, Ó. (2016, October).
Senpy: A Pragmatic Linked Sentiment Analysis Framework.
In Data Science and Advanced Analytics (DSAA),
2016 IEEE International Conference on (pp. 735-742). IEEE.

View File

@ -25,7 +25,7 @@ NIF API
"@context":"http://127.0.0.1/api/contexts/Results.jsonld",
"@id":"_:Results_11241245.22",
"@type":"results"
"activities": [
"analysis": [
"plugins/sentiment-140_0.1"
],
"entries": [
@ -73,7 +73,7 @@ NIF API
.. http:get:: /api/plugins
Returns a list of installed plugins.
**Example request and response**:
**Example request**:
.. sourcecode:: http
@ -82,6 +82,10 @@ NIF API
Accept: application/json, text/javascript
**Example response**:
.. sourcecode:: http
{
"@id": "plugins/sentiment-140_0.1",
"@type": "sentimentPlugin",
@ -139,14 +143,19 @@ NIF API
.. http:get:: /api/plugins/<pluginname>
Returns the information of a specific plugin.
**Example request and response**:
**Example request**:
.. sourcecode:: http
GET /api/plugins/sentiment-random/ HTTP/1.1
GET /api/plugins/rand/ HTTP/1.1
Host: localhost
Accept: application/json, text/javascript
**Example response**:
.. sourcecode:: http
{
"@context": "http://127.0.0.1/api/contexts/ExamplePlugin.jsonld",
"@id": "plugins/ExamplePlugin_0.1",

View File

@ -1,6 +1,5 @@
API and vocabularies
####################
API and Examples
################
.. toctree::
vocabularies.rst

View File

@ -2,7 +2,7 @@
"@context": "http://mixedemotions-project.eu/ns/context.jsonld",
"@id": "me:Result1",
"@type": "results",
"activities": [
"analysis": [
"me:SAnalysis1",
"me:SgAnalysis1",
"me:EmotionAnalysis1",

View File

@ -2,13 +2,17 @@
"@context": "http://mixedemotions-project.eu/ns/context.jsonld",
"@id": "http://example.com#NIFExample",
"@type": "results",
"activities": [
"analysis": [
],
"entries": [
{
"@type": [
"nif:RFC5147String",
"nif:Context"
],
"nif:beginIndex": 0,
"nif:endIndex": 40,
"text": "An entry should have a nif:isString key"
"nif:isString": "My favourite actress is Natalie Portman"
}
]
}

9
docs/commandline.rst Normal file
View File

@ -0,0 +1,9 @@
Command line
============
This video shows how to analyse text directly on the command line using the senpy tool.
.. image:: https://asciinema.org/a/9uwef1ghkjk062cw2t4mhzpyk.png
:width: 100%
:target: https://asciinema.org/a/9uwef1ghkjk062cw2t4mhzpyk
:alt: CLI demo

View File

@ -38,8 +38,6 @@ extensions = [
'sphinxcontrib.httpdomain',
'sphinx.ext.coverage',
'sphinx.ext.autosectionlabel',
'nbsphinx',
'sphinx.ext.mathjax',
]
# Add any paths that contain templates here, relative to this directory.
@ -56,7 +54,7 @@ master_doc = 'index'
# General information about the project.
project = u'Senpy'
copyright = u'2019, J. Fernando Sánchez'
copyright = u'2016, J. Fernando Sánchez'
description = u'A framework for sentiment and emotion analysis services'
# The version info for the project you're documenting, acts as replacement for
@ -81,9 +79,7 @@ language = None
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns = ['_build', '**.ipynb_checkpoints']
exclude_patterns = ['_build']
# The reST default role (used for this markup: `text`) to use for all
# documents.
@ -130,7 +126,6 @@ html_theme_options = {
'github_user': 'gsi-upm',
'github_repo': 'senpy',
'github_banner': True,
'sidebar_collapse': True,
}
@ -291,12 +286,3 @@ texinfo_documents = [
# If true, do not generate a @detailmenu in the "Top" node's menu.
#texinfo_no_detailmenu = False
nbsphinx_prolog = """
.. note:: This is an `auto-generated <https://nbsphinx.readthedocs.io>`_ static view of a Jupyter notebook.
To run the code examples in your computer, you may download the original notebook from the repository: https://github.com/gsi-upm/senpy/tree/master/docs/{{ env.doc2path(env.docname, base=None) }}
----
"""

View File

@ -1,152 +1,93 @@
Automatic Model Conversion
--------------------------
Conversion
----------
Senpy includes support for emotion and sentiment conversion.
When a user requests a specific model, senpy will choose a strategy to convert the model that the service usually outputs and the model requested by the user.
Out of the box, senpy can convert from the `emotionml:pad` (pleasure-arousal-dominance) dimensional model to `emoml:big6` (Ekman's big-6) categories, and vice versa.
This specific conversion uses a series of dimensional centroids (`emotionml:pad`) for each emotion category (`emotionml:big6`).
A dimensional value is converted to a category by looking for the nearest centroid.
The centroids are calculated according to this article:
.. code-block:: text
Kim, S. M., Valitutti, A., & Calvo, R. A. (2010, June).
Evaluation of unsupervised emotion models to textual affect recognition.
In Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text (pp. 62-70).
Association for Computational Linguistics.
It is possible to add new conversion strategies by `Developing a conversion plugin`_.
Senpy includes experimental support for emotion/sentiment conversion plugins.
Use
===
Consider the following query to an emotion service: http://senpy.gsi.upm.es/api/emotion-anew?i=good
Consider the original query: http://127.0.0.1:5000/api/?i=hello&algo=emoRand
The requested plugin (emotion-random) returns emotions using the VAD space (FSRE dimensions in EmotionML):
The requested plugin (emoRand) returns emotions using Ekman's model (or big6 in EmotionML):
.. code:: json
[
... rest of the document ...
{
"@type": "EmotionSet",
"onyx:hasEmotion": [
{
"@type": "Emotion",
"emoml:pad-dimensions_arousal": 5.43,
"emoml:pad-dimensions_dominance": 6.41,
"emoml:pad-dimensions_pleasure": 7.47,
"prov:wasGeneratedBy": "prefix:Analysis_1562744784.8789825"
"@type": "emotionSet",
"onyx:hasEmotion": {
"@type": "emotion",
"onyx:hasEmotionCategory": "emoml:big6anger"
},
"prov:wasGeneratedBy": "plugins/emoRand_0.1"
}
],
"prov:wasGeneratedBy": "prefix:Analysis_1562744784.8789825"
}
]
To get these emotions in VAD space (FSRE dimensions in EmotionML), we'd do this:
To get the equivalent of these emotions in Ekman's categories (i.e., Ekman's Big 6 in EmotionML), we'd do this:
http://senpy.gsi.upm.es/api/emotion-anew?i=good&emotion-model=emoml:big6
http://127.0.0.1:5000/api/?i=hello&algo=emoRand&emotionModel=emoml:fsre-dimensions
This call, provided there is a valid conversion plugin from Ekman's to VAD, would return something like this:
.. code:: json
[
... rest of the document ...
{
"@type": "EmotionSet",
"onyx:hasEmotion": [
{
"@type": "Emotion",
"onyx:algorithmConfidence": 4.4979,
"onyx:hasEmotionCategory": "emoml:big6happiness"
}
],
"prov:wasDerivedFrom": {
"@id": "Emotions0",
"@type": "EmotionSet",
"onyx:hasEmotion": [
{
"@id": "Emotion0",
"@type": "Emotion",
"emoml:pad-dimensions_arousal": 5.43,
"emoml:pad-dimensions_dominance": 6.41,
"emoml:pad-dimensions_pleasure": 7.47,
"prov:wasGeneratedBy": "prefix:Analysis_1562745220.1553965"
}
],
"prov:wasGeneratedBy": "prefix:Analysis_1562745220.1553965"
"@type": "emotionSet",
"onyx:hasEmotion": {
"@type": "emotion",
"onyx:hasEmotionCategory": "emoml:big6anger"
},
"prov:wasGeneratedBy": "prefix:Analysis_1562745220.1570725"
"prov:wasGeneratedBy": "plugins/emoRand_0.1"
}, {
"@type": "emotionSet",
"onyx:hasEmotion": {
"@type": "emotion",
"A": 7.22,
"D": 6.28,
"V": 8.6
},
"prov:wasGeneratedBy": "plugins/Ekman2VAD_0.1"
}
]
That is called a *full* response, as it simply adds the converted emotion alongside.
It is also possible to get the original emotion nested within the new converted emotion, using the `conversion=nested` parameter:
http://senpy.gsi.upm.es/api/emotion-anew?i=good&emotion-model=emoml:big6&conversion=nested
.. code:: json
[
{
"@type": "EmotionSet",
"onyx:hasEmotion": [
{
"@type": "Emotion",
"onyx:algorithmConfidence": 4.4979,
"onyx:hasEmotionCategory": "emoml:big6happiness"
}
],
"prov:wasDerivedFrom": {
"@id": "Emotions0",
"@type": "EmotionSet",
"onyx:hasEmotion": [
{
"@id": "Emotion0",
"@type": "Emotion",
"emoml:pad-dimensions_arousal": 5.43,
"emoml:pad-dimensions_dominance": 6.41,
"emoml:pad-dimensions_pleasure": 7.47,
"prov:wasGeneratedBy": "prefix:Analysis_1562744962.896306"
}
],
"prov:wasGeneratedBy": "prefix:Analysis_1562744962.896306"
},
"prov:wasGeneratedBy": "prefix:Analysis_1562744962.8978968"
}
]
... rest of the document ...
{
"@type": "emotionSet",
"onyx:hasEmotion": {
"@type": "emotion",
"onyx:hasEmotionCategory": "emoml:big6anger"
},
"prov:wasGeneratedBy": "plugins/emoRand_0.1"
"onyx:wasDerivedFrom": {
"@type": "emotionSet",
"onyx:hasEmotion": {
"@type": "emotion",
"A": 7.22,
"D": 6.28,
"V": 8.6
},
"prov:wasGeneratedBy": "plugins/Ekman2VAD_0.1"
}
}
Lastly, `conversion=filtered` would only return the converted emotions.
.. code:: json
[
{
"@type": "EmotionSet",
"onyx:hasEmotion": [
{
"@type": "Emotion",
"onyx:algorithmConfidence": 4.4979,
"onyx:hasEmotionCategory": "emoml:big6happiness"
}
],
"prov:wasGeneratedBy": "prefix:Analysis_1562744925.7322266"
}
]
Developing a conversion plugin
==============================
================================
Conversion plugins are discovered by the server just like any other plugin.
The difference is the slightly different API, and the need to specify the `source` and `target` of the conversion.
@ -165,6 +106,7 @@ For instance, an emotion conversion plugin needs the following:
.. code:: python
@ -172,6 +114,3 @@ For instance, an emotion conversion plugin needs the following:
def convert(self, emotionSet, fromModel, toModel, params):
pass
More implementation details are shown in the `centroids plugin <https://github.com/gsi-upm/senpy/blob/master/senpy/plugins/postprocessing/emotion/centroids.py>`_.

View File

@ -1,13 +1,16 @@
Demo
----
There is a demo available on http://senpy.gsi.upm.es/, where you can test a live instance of Senpy, with several open source plugins.
You can use the playground (a web interface) or the HTTP API.
There is a demo available on http://senpy.cluster.gsi.dit.upm.es/, where you can test a serie of different plugins.
You can use the playground (a web interface) or make HTTP requests to the service API.
.. image:: playground-0.20.png
:target: http://senpy.gsi.upm.es
.. image:: senpy-playground.png
:height: 400px
:width: 800px
:scale: 100 %
:align: center
Plugins Demo
============
The source code and description of the plugins used in the demo are available here: https://github.com/gsi-upm/senpy-plugins-community/.
The source code and description of the plugins used in the demo is available here: https://lab.cluster.gsi.dit.upm.es/senpy/senpy-plugins-community/.

View File

@ -1,25 +0,0 @@
Developing new services
-----------------------
Developing web services can be hard.
A text analysis service must implement all the typical features, such as: extraction of parameters, validation, format conversion, visualization...
Senpy implements all the common blocks, so developers can focus on what really matters: great analysis algorithms that solve real problems.
Among other things, Senpy takes care of these tasks:
* Interfacing with the user: parameter validation, error handling.
* Formatting: JSON-LD, Turtle/n-triples input and output, or simple text input
* Linked Data: senpy results are semantically annotated, using a series of well established vocabularies, and sane default URIs.
* User interface: a web UI where users can explore your service and test different settings
* A client to interact with the service. Currently only available in Python.
You only need to provide the algorithm to turn a piece of text into an annotation
Sharing your sentiment analysis with the world has never been easier!
.. toctree::
:maxdepth: 1
server-cli
plugins-quickstart
plugins-faq
plugins-definition

Binary file not shown.

Before

Width:  |  Height:  |  Size: 77 KiB

Binary file not shown.

Before

Width:  |  Height:  |  Size: 76 KiB

View File

@ -1,6 +1,5 @@
Examples
--------
------
All the examples in this page use the :download:`the main schema <_static/schemas/definitions.json>`.
Simple NIF annotation
@ -18,7 +17,6 @@ Sentiment Analysis
.....................
Description
,,,,,,,,,,,
This annotation corresponds to the sentiment analysis of an input. The example shows the sentiment represented according to Marl format.
The sentiments detected are contained in the Sentiments array with their related part of the text.
@ -26,7 +24,20 @@ Representation
,,,,,,,,,,,,,,
.. literalinclude:: examples/results/example-sentiment.json
:emphasize-lines: 5-11,20-30
:emphasize-lines: 5-10,25-33
:language: json-ld
Suggestion Mining
.................
Description
,,,,,,,,,,,
The suggestions schema represented below shows the suggestions detected in the text. Within it, we can find the NIF fields highlighted that corresponds to the text of the detected suggestion.
Representation
,,,,,,,,,,,,,,
.. literalinclude:: examples/results/example-suggestion.json
:emphasize-lines: 5-8,22-27
:language: json-ld
Emotion Analysis
@ -40,6 +51,28 @@ Representation
.. literalinclude:: examples/results/example-emotion.json
:language: json-ld
:emphasize-lines: 5-11,22-36
:emphasize-lines: 5-8,25-37
Named Entity Recognition
........................
Description
,,,,,,,,,,,
The Named Entity Recognition is represented as follows. In this particular case, it can be seen within the entities array the entities recognised. For the example input, Microsoft and Windows Phone are the ones detected.
Representation
,,,,,,,,,,,,,,
.. literalinclude:: examples/results/example-ner.json
:emphasize-lines: 5-8,19-34
:language: json-ld
Complete example
................
Description
,,,,,,,,,,,
This example covers all of the above cases, integrating all the annotations in the same document.
Representation
,,,,,,,,,,,,,,
.. literalinclude:: examples/results/example-complete.json
:language: json-ld

View File

@ -2,22 +2,11 @@
"@context": "http://mixedemotions-project.eu/ns/context.jsonld",
"@id": "me:Result1",
"@type": "results",
"activities": [
{
"@id": "_:SAnalysis1_Activity",
"@type": "marl:SentimentAnalysis",
"prov:wasAssociatedWith": "me:SAnalysis1"
},
{
"@id": "_:EmotionAnalysis1_Activity",
"@type": "onyx:EmotionAnalysis",
"prov:wasAssociatedWith": "me:EmotionAnalysis1"
},
{
"@id": "_:NER1_Activity",
"@type": "me:NER",
"prov:wasAssociatedWith": "me:NER1"
}
"analysis": [
"me:SAnalysis1",
"me:SgAnalysis1",
"me:EmotionAnalysis1",
"me:NER1"
],
"entries": [
{
@ -34,7 +23,7 @@
"nif:endIndex": 13,
"nif:anchorOf": "Microsoft",
"me:references": "http://dbpedia.org/page/Microsoft",
"prov:wasGeneratedBy": "_:NER1_Activity"
"prov:wasGeneratedBy": "me:NER1"
},
{
"@id": "http://micro.blog/status1#char=25,37",
@ -42,7 +31,7 @@
"nif:endIndex": 37,
"nif:anchorOf": "Windows Phone",
"me:references": "http://dbpedia.org/page/Windows_Phone",
"prov:wasGeneratedBy": "_:NER1_Activity"
"prov:wasGeneratedBy": "me:NER1"
}
],
"suggestions": [
@ -51,7 +40,7 @@
"nif:beginIndex": 16,
"nif:endIndex": 77,
"nif:anchorOf": "put your Windows Phone on your newest #open technology program",
"prov:wasGeneratedBy": "_:SgAnalysis1_Activity"
"prov:wasGeneratedBy": "me:SgAnalysis1"
}
],
"sentiments": [
@ -62,14 +51,14 @@
"nif:anchorOf": "You'll be awesome.",
"marl:hasPolarity": "marl:Positive",
"marl:polarityValue": 0.9,
"prov:wasGeneratedBy": "_:SgAnalysis1_Activity"
"prov:wasGeneratedBy": "me:SAnalysis1"
}
],
"emotions": [
{
"@id": "http://micro.blog/status1#char=0,109",
"nif:anchorOf": "Dear Microsoft, put your Windows Phone on your newest #open technology program. You'll be awesome. #opensource",
"prov:wasGeneratedBy": "_:EmotionAnalysis1_Activity",
"prov:wasGeneratedBy": "me:EAnalysis1",
"onyx:hasEmotion": [
{
"onyx:hasEmotionCategory": "wna:liking"

View File

@ -0,0 +1,78 @@
{
"@context": "http://mixedemotions-project.eu/ns/context.jsonld",
"@id": "me:Result1",
"@type": "results",
"analysis": [
"me:SAnalysis1",
"me:SgAnalysis1",
"me:EmotionAnalysis1",
"me:NER1",
{
"@type": "analysis",
"@id": "anonymous"
}
],
"entries": [
{
"@id": "http://micro.blog/status1",
"@type": [
"nif:RFC5147String",
"nif:Context"
],
"nif:isString": "Dear Microsoft, put your Windows Phone on your newest #open technology program. You'll be awesome. #opensource",
"entities": [
{
"@id": "http://micro.blog/status1#char=5,13",
"nif:beginIndex": 5,
"nif:endIndex": 13,
"nif:anchorOf": "Microsoft",
"me:references": "http://dbpedia.org/page/Microsoft",
"prov:wasGeneratedBy": "me:NER1"
},
{
"@id": "http://micro.blog/status1#char=25,37",
"nif:beginIndex": 25,
"nif:endIndex": 37,
"nif:anchorOf": "Windows Phone",
"me:references": "http://dbpedia.org/page/Windows_Phone",
"prov:wasGeneratedBy": "me:NER1"
}
],
"suggestions": [
{
"@id": "http://micro.blog/status1#char=16,77",
"nif:beginIndex": 16,
"nif:endIndex": 77,
"nif:anchorOf": "put your Windows Phone on your newest #open technology program",
"prov:wasGeneratedBy": "me:SgAnalysis1"
}
],
"sentiments": [
{
"@id": "http://micro.blog/status1#char=80,97",
"nif:beginIndex": 80,
"nif:endIndex": 97,
"nif:anchorOf": "You'll be awesome.",
"marl:hasPolarity": "marl:Positive",
"marl:polarityValue": 0.9,
"prov:wasGeneratedBy": "me:SAnalysis1"
}
],
"emotions": [
{
"@id": "http://micro.blog/status1#char=0,109",
"nif:anchorOf": "Dear Microsoft, put your Windows Phone on your newest #open technology program. You'll be awesome. #opensource",
"prov:wasGeneratedBy": "me:EAnalysis1",
"onyx:hasEmotion": [
{
"onyx:hasEmotionCategory": "wna:liking"
},
{
"onyx:hasEmotionCategory": "wna:excitement"
}
]
}
]
}
]
}

View File

@ -1,8 +1,9 @@
{
"@context": "http://mixedemotions-project.eu/ns/context.jsonld",
"@id": "me:Result1",
"@id": "http://example.com#NIFExample",
"@type": "results",
"activities": [ ],
"analysis": [
],
"entries": [
{
"@id": "http://example.org#char=0,40",

View File

@ -2,36 +2,24 @@
"@context": "http://mixedemotions-project.eu/ns/context.jsonld",
"@id": "me:Result1",
"@type": "results",
"activities": [
"analysis": [
{
"@id": "_:SAnalysis1_Activity",
"@id": "me:SAnalysis1",
"@type": "marl:SentimentAnalysis",
"prov:wasAssociatedWith": "me:SentimentAnalysis",
"prov:used": [
{
"name": "marl:maxPolarityValue",
"prov:value": "1"
"marl:maxPolarityValue": 1,
"marl:minPolarityValue": 0
},
{
"name": "marl:minPolarityValue",
"prov:value": "0"
}
]
},
{
"@id": "_:SgAnalysis1_Activity",
"prov:wasAssociatedWith": "me:SgAnalysis1",
"@id": "me:SgAnalysis1",
"@type": "me:SuggestionAnalysis"
},
{
"@id": "_:EmotionAnalysis1_Activity",
"@type": "me:EmotionAnalysis",
"prov:wasAssociatedWith": "me:EmotionAnalysis1"
"@id": "me:EmotionAnalysis1",
"@type": "me:EmotionAnalysis"
},
{
"@id": "_:NER1_Activity",
"@type": "me:NER",
"prov:wasAssociatedWith": "me:EmotionNER1"
"@id": "me:NER1",
"@type": "me:NER"
}
],
"entries": [

View File

@ -2,11 +2,10 @@
"@context": "http://mixedemotions-project.eu/ns/context.jsonld",
"@id": "me:Result1",
"@type": "results",
"activities": [
"analysis": [
{
"@id": "me:EmotionAnalysis1_Activity",
"@type": "me:EmotionAnalysis1",
"prov:wasAssociatedWith": "me:EmotionAnalysis1"
"@id": "me:EmotionAnalysis1",
"@type": "onyx:EmotionAnalysis"
}
],
"entries": [
@ -17,13 +16,17 @@
"nif:Context"
],
"nif:isString": "Dear Microsoft, put your Windows Phone on your newest #open technology program. You'll be awesome. #opensource",
"entities": [
],
"suggestions": [
],
"sentiments": [
],
"emotions": [
{
"@id": "http://micro.blog/status1#char=0,109",
"nif:anchorOf": "Dear Microsoft, put your Windows Phone on your newest #open technology program. You'll be awesome. #opensource",
"prov:wasGeneratedBy": "_:EmotionAnalysis1_Activity",
"prov:wasGeneratedBy": "me:EmotionAnalysis1",
"onyx:hasEmotion": [
{
"onyx:hasEmotionCategory": "wna:liking"

View File

@ -2,11 +2,10 @@
"@context": "http://mixedemotions-project.eu/ns/context.jsonld",
"@id": "me:Result1",
"@type": "results",
"activities": [
"analysis": [
{
"@id": "_:NER1_Activity",
"@type": "me:NERAnalysis",
"prov:wasAssociatedWith": "me:NER1"
"@id": "me:NER1",
"@type": "me:NERAnalysis"
}
],
"entries": [

View File

@ -2,22 +2,16 @@
"@context": [
"http://mixedemotions-project.eu/ns/context.jsonld",
{
"emovoc": "http://www.gsi.upm.es/ontologies/onyx/vocabularies/emotionml/ns#"
"emovoc": "http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/emotionml/ns#"
}
],
"@id": "me:Result1",
"@type": "results",
"activities": [
"analysis": [
{
"@id": "me:HesamsAnalysis_Activity",
"@id": "me:HesamsAnalysis",
"@type": "onyx:EmotionAnalysis",
"prov:wasAssociatedWith": "me:HesamsAnalysis",
"prov:used": [
{
"name": "emotion-model",
"prov:value": "emovoc:pad-dimensions"
}
]
"onyx:usesEmotionModel": "emovoc:pad-dimensions"
}
],
"entries": [
@ -38,7 +32,7 @@
{
"@id": "Entry1#char=0,21",
"nif:anchorOf": "This is a test string",
"prov:wasGeneratedBy": "_:HesamAnalysis_Activity",
"prov:wasGeneratedBy": "me:HesamAnalysis",
"onyx:hasEmotion": [
{
"emovoc:pleasure": 0.5,

View File

@ -2,11 +2,12 @@
"@context": "http://mixedemotions-project.eu/ns/context.jsonld",
"@id": "me:Result1",
"@type": "results",
"activities": [
"analysis": [
{
"@id": "_:SAnalysis1_Activity",
"@id": "me:SAnalysis1",
"@type": "marl:SentimentAnalysis",
"prov:wasAssociatedWith": "me:SAnalysis1"
"marl:maxPolarityValue": 1,
"marl:minPolarityValue": 0
}
],
"entries": [
@ -17,6 +18,10 @@
"nif:Context"
],
"nif:isString": "Dear Microsoft, put your Windows Phone on your newest #open technology program. You'll be awesome. #opensource",
"entities": [
],
"suggestions": [
],
"sentiments": [
{
"@id": "http://micro.blog/status1#char=80,97",
@ -25,10 +30,10 @@
"nif:anchorOf": "You'll be awesome.",
"marl:hasPolarity": "marl:Positive",
"marl:polarityValue": 0.9,
"prov:wasGeneratedBy": "_:SAnalysis1_Activity"
"prov:wasGeneratedBy": "me:SAnalysis1"
}
],
"emotions": [
"emotionSets": [
]
}
]

View File

@ -2,12 +2,8 @@
"@context": "http://mixedemotions-project.eu/ns/context.jsonld",
"@id": "me:Result1",
"@type": "results",
"activities": [
{
"@id": "_:SgAnalysis1_Activity",
"@type": "me:SuggestionAnalysis",
"prov:wasAssociatedWith": "me:SgAnalysis1"
}
"analysis": [
"me:SgAnalysis1"
],
"entries": [
{
@ -16,6 +12,7 @@
"nif:RFC5147String",
"nif:Context"
],
"prov:wasGeneratedBy": "me:SAnalysis1",
"nif:isString": "Dear Microsoft, put your Windows Phone on your newest #open technology program. You'll be awesome. #opensource",
"entities": [
],
@ -25,7 +22,7 @@
"nif:beginIndex": 16,
"nif:endIndex": 77,
"nif:anchorOf": "put your Windows Phone on your newest #open technology program",
"prov:wasGeneratedBy": "_:SgAnalysis1_Activity"
"prov:wasGeneratedBy": "me:SgAnalysis1"
}
],
"sentiments": [

View File

@ -1,106 +1,35 @@
Welcome to Senpy's documentation!
=================================
.. image:: https://readthedocs.org/projects/senpy/badge/?version=latest
:target: http://senpy.readthedocs.io/en/latest/
.. image:: https://badge.fury.io/py/senpy.svg
:target: https://badge.fury.io/py/senpy
.. image:: https://travis-ci.org/gsi-upm/senpy.svg
:target: https://github.com/gsi-upm/senpy/senpy/tree/master
.. image:: https://lab.cluster.gsi.dit.upm.es/senpy/senpy/badges/master/build.svg
:target: https://lab.cluster.gsi.dit.upm.es/senpy/senpy/commits/master
.. image:: https://lab.cluster.gsi.dit.upm.es/senpy/senpy/badges/master/coverage.svg
:target: https://lab.cluster.gsi.dit.upm.es/senpy/senpy/commits/master
.. image:: https://img.shields.io/pypi/l/requests.svg
:target: https://lab.gsi.upm.es/senpy/senpy/
Senpy is a framework to build sentiment and emotion analysis services.
It provides functionalities for:
- developing sentiment and emotion classifier and exposing them as an HTTP service
- requesting sentiment and emotion analysis from different providers (i.e. Vader, Sentimet140, ...) using the same interface (:doc:`apischema`). In this way, applications do not depend on the API offered for these services.
- combining services that use different sentiment model (e.g. polarity between [-1, 1] or [0,1] or emotion models (e.g. Ekkman or VAD)
- evaluating sentiment algorithms with well known datasets
Using senpy services is as simple as sending an HTTP request with your favourite tool or library.
Let's analyze the sentiment of the text "Senpy is awesome".
We can call the `Sentiment140 <http://www.sentiment140.com/>`_ service with an HTTP request using curl:
.. code:: shell
:emphasize-lines: 14,18
$ curl "http://senpy.gsi.upm.es/api/sentiment140" \
--data-urlencode "input=Senpy is awesome"
{
"@context": "http://senpy.gsi.upm.es/api/contexts/YXBpL3NlbnRpbWVudDE0MD8j",
"@type": "Results",
"entries": [
{
"@id": "prefix:",
"@type": "Entry",
"marl:hasOpinion": [
{
"@type": "Sentiment",
"marl:hasPolarity": "marl:Positive",
"prov:wasGeneratedBy": "prefix:Analysis_1554389334.6431913"
}
],
"nif:isString": "Senpy is awesome",
"onyx:hasEmotionSet": []
}
]
}
Congratulations, youve used your first senpy service!
You can observe the result: the polarity is positive (marl:Positive). The reason of this prefix is that Senpy follows a linked data approach.
You can analyze the same sentence using a different sentiment service (e.g. Vader) and requesting a different format (e.g. turtle):
:target: https://lab.cluster.gsi.dit.upm.es/senpy/senpy/
.. code:: shell
Senpy is a framework for sentiment and emotion analysis services.
Services built with senpy are interchangeable and easy to use because they share a common :doc:`apischema`.
It also simplifies service development.
$ curl "http://senpy.gsi.upm.es/api/sentiment-vader" \
--data-urlencode "input=Senpy is awesome" \
--data-urlencode "outformat=turtle"
@prefix : <http://www.gsi.upm.es/onto/senpy/ns#> .
@prefix endpoint: <http://senpy.gsi.upm.es/api/> .
@prefix marl: <http://www.gsi.upm.es/ontologies/marl/ns#> .
@prefix nif: <http://persistence.uni-leipzig.org/nlp2rdf/ontologies/nif-core#> .
@prefix prefix: <http://senpy.invalid/> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix senpy: <http://www.gsi.upm.es/onto/senpy/ns#> .
prefix: a senpy:Entry ;
nif:isString "Senpy is awesome" ;
marl:hasOpinion [ a senpy:Sentiment ;
marl:hasPolarity "marl:Positive" ;
marl:polarityValue 6.72e-01 ;
prov:wasGeneratedBy prefix:Analysis_1562668175.9808676 ] .
[] a senpy:Results ;
prov:used prefix: .
As you see, Vader returns also the polarity value (0.67) in addition to the category (positive).
If you are interested in consuming Senpy services, read :doc:`Quickstart`.
To get familiar with the concepts behind Senpy, and what it can offer for service developers, check out :doc:`development`.
:doc:`apischema` contains information about the semantic models and vocabularies used by Senpy.
.. image:: senpy-architecture.png
:width: 100%
:align: center
.. toctree::
:caption: Learn more about senpy:
:maxdepth: 2
:hidden:
senpy
demo
Quickstart.ipynb
installation
conversion
Evaluation.ipynb
demo
usage
apischema
development
publications
projects
plugins
conversion
about

View File

@ -1,10 +1,10 @@
Installation
------------
The stable version can be used in two ways: as a system/user library through pip, or from a docker image.
The stable version can be used in two ways: as a system/user library through pip, or as a docker image.
Using docker is recommended because the image is self-contained, reproducible and isolated from the system, which means:
The docker image is the recommended way because it is self-contained and isolated from the system, which means:
* It can be downloaded and run with just one simple command
* Downloading and using it is just one command
* All dependencies are included
* It is OS-independent (MacOS, Windows, GNU/Linux)
* Several versions may coexist in the same machine without additional virtual environments
@ -17,39 +17,42 @@ Through PIP
.. code:: bash
pip install senpy
# Or with --user if you get permission errors:
pip install --user senpy
..
Alternatively, you can use the development version:
.. code:: bash
Alternatively, you can use the development version:
.. code:: bash
git clone git@github.com:gsi-upm/senpy
cd senpy
pip install --user .
Each version is automatically tested on GNU/Linux, macOS and Windows 10.
If you have trouble with the installation, please file an `issue on GitHub <https://github.com/gsi-upm/senpy/issues>`_.
If you want to install senpy globally, use sudo instead of the ``--user`` flag.
Docker Image
************
The base image of senpy comes with some built-in plugins that you can use:
Build the image or use the pre-built one:
.. code:: bash
docker run -ti -p 5000:5000 gsiupm/senpy --host 0.0.0.0
docker run -ti -p 5000:5000 gsiupm/senpy --host 0.0.0.0 --default-plugins
To use your custom plugins, you can add volume to the container:
To add custom plugins, use a docker volume:
.. code:: bash
docker run -ti -p 5000:5000 -v <PATH OF PLUGINS>:/plugins gsiupm/senpy --host 0.0.0.0 --plugins-folder /plugins
docker run -ti -p 5000:5000 -v <PATH OF PLUGINS>:/plugins gsiupm/senpy --host 0.0.0.0 --default-plugins -f /plugins
Python 2
........
There is a Senpy version for python2 too:
.. code:: bash
docker run -ti -p 5000:5000 gsiupm/senpy:python2.7 --host 0.0.0.0 --default-plugins
Alias
@ -59,7 +62,7 @@ If you are using the docker approach regularly, it is advisable to use a script
.. code:: bash
alias senpy='docker run --rm -ti -p 5000:5000 -v $PWD:/senpy-plugins gsiupm/senpy'
alias senpy='docker run --rm -ti -p 5000:5000 -v $PWD:/senpy-plugins gsiupm/senpy --default-plugins'
Now, you may run senpy from any folder in your computer like so:

Binary file not shown.

Before

Width:  |  Height:  |  Size: 68 KiB

View File

@ -1,112 +0,0 @@
Advanced plugin definition
--------------------------
In addition to finding plugins defined in source code files, senpy can also load a special type of definition file (`.senpy` files).
This used to be the only mechanism for loading in earlier versions of senpy.
The definition file contains basic information
Lastly, it is also possible to add new plugins programmatically.
.. contents:: :local:
..
What is a plugin?
=================
A plugin is a program that, given a text, will add annotations to it.
In practice, a plugin consists of at least two files:
- Definition file: a `.senpy` file that describes the plugin (e.g. what input parameters it accepts, what emotion model it uses).
- Python module: the actual code that will add annotations to each input.
This separation allows us to deploy plugins that use the same code but employ different parameters.
For instance, one could use the same classifier and processing in several plugins, but train with different datasets.
This scenario is particularly useful for evaluation purposes.
The only limitation is that the name of each plugin needs to be unique.
Definition files
================
The definition file complements and overrides the attributes provided by the plugin.
It can be written in YAML or JSON.
The most important attributes are:
* **name**: unique name that senpy will use internally to identify the plugin.
* **module**: indicates the module that contains the plugin code, which will be automatically loaded by senpy.
* **version**
* extra_params: to add parameters to the senpy API when this plugin is requested. Those parameters may be required, and have aliased names. For instance:
.. code:: yaml
extra_params:
hello_param:
aliases: # required
- hello_param
- hello
required: true
default: Hi you
values:
- Hi you
- Hello y'all
- Howdy
A complete example:
.. code:: yaml
name: <Name of the plugin>
module: <Python file>
version: 0.1
And the json equivalent:
.. code:: json
{
"name": "<Name of the plugin>",
"module": "<Python file>",
"version": "0.1"
}
Example plugin with a definition file
=====================================
In this section, we will implement a basic sentiment analysis plugin.
To determine the polarity of each entry, the plugin will compare the length of the string to a threshold.
This threshold will be included in the definition file.
The definition file would look like this:
.. code:: yaml
name: helloworld
module: helloworld
version: 0.0
threshold: 10
description: Hello World
Now, in a file named ``helloworld.py``:
.. code:: python
#!/bin/env python
#helloworld.py
from senpy import AnalysisPlugin
from senpy import Sentiment
class HelloWorld(AnalysisPlugin):
def analyse_entry(entry, params):
'''Basically do nothing with each entry'''
sentiment = Sentiment()
if len(entry.text) < self.threshold:
sentiment['marl:hasPolarity'] = 'marl:Positive'
else:
sentiment['marl:hasPolarity'] = 'marl:Negative'
entry.sentiments.append(sentiment)
yield entry

View File

@ -1,258 +0,0 @@
F.A.Q.
======
.. contents:: :local:
What are annotations?
#####################
They are objects just like entries.
Senpy ships with several default annotations, including ``Sentiment`` and ``Emotion``.
What's a plugin made of?
########################
When receiving a query, senpy selects what plugin or plugins should process each entry, and in what order.
It also makes sure the every entry and the parameters provided by the user meet the plugin requirements.
Hence, two parts are necessary: 1) the code that will process the entry, and 2) some attributes and metadata that will tell senpy how to interact with the plugin.
In practice, this is what a plugin looks like, tests included:
.. literalinclude:: ../example-plugins/rand_plugin.py
:emphasize-lines: 21-28
:language: python
The lines highlighted contain some information about the plugin.
In particular, the following information is mandatory:
* A unique name for the class. In our example, sentiment-random.
* The subclass/type of plugin. This is typically either `SentimentPlugin` or `EmotionPlugin`. However, new types of plugin can be created for different annotations. The only requirement is that these new types inherit from `senpy.Analysis`
* A description of the plugin. This can be done simply by adding a doc to the class.
* A version, which should get updated.
* An author name.
How does senpy find modules?
############################
Senpy looks for files of two types:
* Python files of the form `senpy_<NAME>.py` or `<NAME>_plugin.py`. In these files, it will look for: 1) Instances that inherit from `senpy.Plugin`, or subclasses of `senpy.Plugin` that can be initialized without a configuration file. i.e. classes that contain all the required attributes for a plugin.
* Plugin definition files (see :doc:`plugins-definition`)
How can I define additional parameters for my plugin?
#####################################################
Your plugin may ask for additional parameters from users by using the attribute ``extra_params`` in your plugin definition.
It takes a dictionary, where the keys are the name of the argument/parameter, and the value has the following fields:
* aliases: the different names which can be used in the request to use the parameter.
* required: if set to true, users need to provide this parameter unless a default is set.
* options: the different acceptable values of the parameter (i.e. an enum). If set, the value provided must match one of the options.
* default: the default value of the parameter, if none is provided in the request.
.. code:: python
"extra_params":{
"language": {
"aliases": ["language", "lang", "l"],
"required": True,
"options": ["es", "en"],
"default": "es"
}
}
How should I load external data and files
#########################################
Most plugins will need access to files (dictionaries, lexicons, etc.).
These files are usually heavy or under a license that does not allow redistribution.
For this reason, senpy has a `data_folder` that is separated from the source files.
The location of this folder is controlled programmatically or by setting the `SENPY_DATA` environment variable.
You can use the `self.path(filepath)` function to get the path of a given `filepath` within the data folder.
Plugins have a convenience function `self.open` which will automatically look for the file if it exists, or open a new one if it doesn't:
.. code:: python
import os
class PluginWithResources(AnalysisPlugin):
file_in_data = <FILE PATH>
file_in_sources = <FILE PATH>
def on activate(self):
with self.open(self.file_in_data) as f:
self._classifier = train_from_file(f)
file_in_source = os.path.join(self.get_folder(), self.file_in_sources)
with self.open(file_in_source) as f:
pass
It is good practice to specify the paths of these files in the plugin configuration, so the same code can be reused with different resources.
Can I build a docker image for my plugin?
#########################################
Add the following dockerfile to your project to generate a docker image with your plugin:
.. code:: dockerfile
FROM gsiupm/senpy
Once you make sure your plugin works with a specific version of senpy, modify that file to make sure your build will work even if senpy gets updated.
e.g.:
.. code:: dockerfile
FROM gsiupm/senpy:1.0.1
This will copy your source folder to the image, and install all dependencies.
Now, to build an image:
.. code:: shell
docker build . -t gsiupm/exampleplugin
And you can run it with:
.. code:: shell
docker run -p 5000:5000 gsiupm/exampleplugin
If the plugin uses non-source files (:ref:`How should I load external data and files`), the recommended way is to use `SENPY_DATA` folder.
Data can then be mounted in the container or added to the image.
The former is recommended for open source plugins with licensed resources, whereas the latter is the most convenient and can be used for private images.
Mounting data:
.. code:: bash
docker run -v $PWD/data:/data gsiupm/exampleplugin
Adding data to the image:
.. code:: dockerfile
FROM gsiupm/senpy:1.0.1
COPY data /
What annotations can I use?
###########################
You can add almost any annotation to an entry.
The most common use cases are covered in the :doc:`apischema`.
Why does the analyse function yield instead of return?
######################################################
This is so that plugins may add new entries to the response or filter some of them.
For instance, a chunker may split one entry into several.
On the other hand, a conversion plugin may leave out those entries that do not contain relevant information.
If I'm using a classifier, where should I train it?
###################################################
Training a classifier can be time time consuming. To avoid running the training unnecessarily, you can use ShelfMixin to store the classifier. For instance:
.. code:: python
from senpy.plugins import ShelfMixin, AnalysisPlugin
class MyPlugin(ShelfMixin, AnalysisPlugin):
def train(self):
''' Code to train the classifier
'''
# Here goes the code
# ...
return classifier
def activate(self):
if 'classifier' not in self.sh:
classifier = self.train()
self.sh['classifier'] = classifier
self.classifier = self.sh['classifier']
def deactivate(self):
self.close()
By default the ShelfMixin creates a file based on the plugin name and stores it in that plugin's folder.
However, you can manually specify a 'shelf_file' in your .senpy file.
Shelves may get corrupted if the plugin exists unexpectedly.
A corrupt shelf prevents the plugin from loading.
If you do not care about the data in the shelf, you can force your plugin to remove the corrupted file and load anyway, set the 'force_shelf' to True in your plugin and start it again.
How can I turn an external service into a plugin?
#################################################
This example ilustrate how to implement a plugin that accesses the Sentiment140 service.
.. code:: python
class Sentiment140Plugin(SentimentPlugin):
def analyse_entry(self, entry, params):
text = entry.text
lang = params.get("language", "auto")
res = requests.post("http://www.sentiment140.com/api/bulkClassifyJson",
json.dumps({"language": lang,
"data": [{"text": text}]
}
)
)
p = params.get("prefix", None)
polarity_value = self.maxPolarityValue*int(res.json()["data"][0]
["polarity"]) * 0.25
polarity = "marl:Neutral"
neutral_value = self.maxPolarityValue / 2.0
if polarity_value > neutral_value:
polarity = "marl:Positive"
elif polarity_value < neutral_value:
polarity = "marl:Negative"
sentiment = Sentiment(id="Sentiment0",
prefix=p,
marl__hasPolarity=polarity,
marl__polarityValue=polarity_value)
sentiment.prov(self)
entry.sentiments.append(sentiment)
yield entry
How can I activate a DEBUG mode for my plugin?
###############################################
You can activate the DEBUG mode by the command-line tool using the option -d.
.. code:: bash
senpy -d
Additionally, with the ``--pdb`` option you will be dropped into a pdb post mortem shell if an exception is raised.
.. code:: bash
python -m pdb yourplugin.py
Where can I find more code examples?
####################################
See: `<http://github.com/gsi-upm/senpy-plugins-community>`_.

View File

@ -1,87 +0,0 @@
Quickstart for service developers
=================================
This document contains the minimum to get you started with developing new services using Senpy.
For an example of conversion plugins, see :doc:`conversion`.
For a description of definition files, see :doc:`plugins-definition`.
A more step-by-step tutorial with slides is available `here <https://lab.gsi.upm.es/senpy/senpy-tutorial>`__
.. contents:: :local:
Installation
############
First of all, you need to install the package.
See :doc:`installation` for instructions.
Once installed, the `senpy` command should be available.
Architecture
############
The main component of a sentiment analysis service is the algorithm itself. However, for the algorithm to work, it needs to get the appropriate parameters from the user, format the results according to the defined API, interact with the user whn errors occur or more information is needed, etc.
Senpy proposes a modular and dynamic architecture that allows:
* Implementing different algorithms in a extensible way, yet offering a common interface.
* Offering common services that facilitate development, so developers can focus on implementing new and better algorithms.
The framework consists of two main modules: Senpy core, which is the building block of the service, and Senpy plugins, which consist of the analysis algorithm. The next figure depicts a simplified version of the processes involved in an analysis with the Senpy framework.
.. image:: senpy-architecture.png
:width: 100%
:align: center
What is a plugin?
#################
A plugin is a python object that can process entries.
Given an entry, it will modify it, add annotations to it, or generate new entries.
What is an entry?
#################
Entries are objects that can be annotated.
In general, they will be a piece of text.
By default, entries are `NIF contexts <http://persistence.uni-leipzig.org/nlp2rdf/ontologies/nif-core/nif-core.html>`_ represented in JSON-LD format.
It is a dictionary/JSON object that looks like this:
.. code:: python
{
"@id": "<unique identifier or blank node name>",
"nif:isString": "input text",
"sentiments": [ {
...
}
],
...
}
Annotations are added to the object like this:
.. code:: python
entry = Entry()
entry.vocabulary__annotationName = 'myvalue'
entry['vocabulary:annotationName'] = 'myvalue'
entry['annotationNameURI'] = 'myvalue'
Where vocabulary is one of the prefixes defined in the default senpy context, and annotationURI is a full URI.
The value may be any valid JSON-LD dictionary.
For simplicity, senpy includes a series of models by default in the ``senpy.models`` module.
Plugins Code
############
The basic methods in a plugin are:
* analyse_entry: called in every user requests. It takes two parameters: ``Entry``, the entry object, and ``params``, the parameters supplied by the user. It should yield one or more ``Entry`` objects.
* activate: used to load memory-hungry resources. For instance, to train a classifier.
* deactivate: used to free up resources when the plugin is no longer needed.
Plugins are loaded asynchronously, so don't worry if the activate method takes too long. The plugin will be marked as activated once it is finished executing the method.

379
docs/plugins.rst Normal file
View File

@ -0,0 +1,379 @@
Developing new plugins
----------------------
This document describes how to develop a new analysis plugin. For an example of conversion plugins, see :doc:`conversion`.
A more step-by-step tutorial with slides is available `here <https://lab.cluster.gsi.dit.upm.es/senpy/senpy-tutorial>`__
.. contents:: :local:
What is a plugin?
=================
A plugin is a program that, given a text, will add annotations to it.
In practice, a plugin consists of at least two files:
- Definition file: a `.senpy` file that describes the plugin (e.g. what input parameters it accepts, what emotion model it uses).
- Python module: the actual code that will add annotations to each input.
This separation allows us to deploy plugins that use the same code but employ different parameters.
For instance, one could use the same classifier and processing in several plugins, but train with different datasets.
This scenario is particularly useful for evaluation purposes.
The only limitation is that the name of each plugin needs to be unique.
Plugin Definition files
=======================
The definition file contains all the attributes of the plugin, and can be written in YAML or JSON.
When the server is launched, it will recursively search for definition files in the plugin folder (the current folder, by default).
The most important attributes are:
* **name**: unique name that senpy will use internally to identify the plugin.
* **module**: indicates the module that contains the plugin code, which will be automatically loaded by senpy.
* **version**
* extra_params: to add parameters to the senpy API when this plugin is requested. Those parameters may be required, and have aliased names. For instance:
.. code:: yaml
extra_params:
hello_param:
aliases: # required
- hello_param
- hello
required: true
default: Hi you
values:
- Hi you
- Hello y'all
- Howdy
Parameter validation will fail if a required parameter without a default has not been provided, or if the definition includes a set of values and the provided one does not match one of them.
A complete example:
.. code:: yaml
name: <Name of the plugin>
module: <Python file>
version: 0.1
And the json equivalent:
.. code:: json
{
"name": "<Name of the plugin>",
"module": "<Python file>",
"version": "0.1"
}
Plugins Code
============
The basic methods in a plugin are:
* __init__
* activate: used to load memory-hungry resources
* deactivate: used to free up resources
* analyse_entry: called in every user requests. It takes two parameters: ``Entry``, the entry object, and ``params``, the parameters supplied by the user. It should yield one or more ``Entry`` objects.
Plugins are loaded asynchronously, so don't worry if the activate method takes too long. The plugin will be marked as activated once it is finished executing the method.
Entries
=======
Entries are objects that can be annotated.
By default, entries are `NIF contexts <http://persistence.uni-leipzig.org/nlp2rdf/ontologies/nif-core/nif-core.html>`_ represented in JSON-LD format.
Annotations are added to the object like this:
.. code:: python
entry = Entry()
entry.vocabulary__annotationName = 'myvalue'
entry['vocabulary:annotationName'] = 'myvalue'
entry['annotationNameURI'] = 'myvalue'
Where vocabulary is one of the prefixes defined in the default senpy context, and annotationURI is a full URI.
The value may be any valid JSON-LD dictionary.
For simplicity, senpy includes a series of models by default in the ``senpy.models`` module.
Example plugin
==============
In this section, we will implement a basic sentiment analysis plugin.
To determine the polarity of each entry, the plugin will compare the length of the string to a threshold.
This threshold will be included in the definition file.
The definition file would look like this:
.. code:: yaml
name: helloworld
module: helloworld
version: 0.0
threshold: 10
description: Hello World
Now, in a file named ``helloworld.py``:
.. code:: python
#!/bin/env python
#helloworld.py
from senpy.plugins import AnalysisPlugin
from senpy.models import Sentiment
class HelloWorld(AnalysisPlugin):
def analyse_entry(entry, params):
'''Basically do nothing with each entry'''
sentiment = Sentiment()
if len(entry.text) < self.threshold:
sentiment['marl:hasPolarity'] = 'marl:Positive'
else:
sentiment['marl:hasPolarity'] = 'marl:Negative'
entry.sentiments.append(sentiment)
yield entry
The complete code of the example plugin is available `here <https://lab.cluster.gsi.dit.upm.es/senpy/plugin-prueba>`__.
Loading data and files
======================
Most plugins will need access to files (dictionaries, lexicons, etc.).
It is good practice to specify the paths of these files in the plugin configuration, so the same code can be reused with different resources.
.. code:: yaml
name: dictworld
module: dictworld
dictionary_path: <PATH OF THE FILE>
The path can be either absolute, or relative.
From absolute paths
???????????????????
Absolute paths (such as ``/data/dictionary.csv`` are straightfoward:
.. code:: python
with open(os.path.join(self.dictionary_path) as f:
...
From relative paths
???????????????????
Since plugins are loading dynamically, relative paths will refer to the current working directory.
Instead, what you usually want is to load files *relative to the plugin source folder*, like so:
::
.
..
plugin.senpy
plugin.py
dictionary.csv
For this, we need to first get the path of your source folder first, like so:
.. code:: python
import os
root = os.path.realpath(__file__)
with open(os.path.join(root, self.dictionary_path) as f:
...
Docker image
============
Add the following dockerfile to your project to generate a docker image with your plugin:
.. code:: dockerfile
FROM gsiupm/senpy:0.8.8
This will copy your source folder to the image, and install all dependencies.
Now, to build an image:
.. code:: shell
docker build . -t gsiupm/exampleplugin
And you can run it with:
.. code:: shell
docker run -p 5000:5000 gsiupm/exampleplugin
If the plugin non-source files (:ref:`loading data and files`), the recommended way is to use absolute paths.
Data can then be mounted in the container or added to the image.
The former is recommended for open source plugins with licensed resources, whereas the latter is the most convenient and can be used for private images.
Mounting data:
.. code:: bash
docker run -v $PWD/data:/data gsiupm/exampleplugin
Adding data to the image:
.. code:: dockerfile
FROM gsiupm/senpy:0.8.8
COPY data /
F.A.Q.
======
What annotations can I use?
???????????????????????????
You can add almost any annotation to an entry.
The most common use cases are covered in the :doc:`apischema`.
Why does the analyse function yield instead of return?
??????????????????????????????????????????????????????
This is so that plugins may add new entries to the response or filter some of them.
For instance, a `context detection` plugin may add a new entry for each context in the original entry.
On the other hand, a conversion plugin may leave out those entries that do not contain relevant information.
If I'm using a classifier, where should I train it?
???????????????????????????????????????????????????
Training a classifier can be time time consuming. To avoid running the training unnecessarily, you can use ShelfMixin to store the classifier. For instance:
.. code:: python
from senpy.plugins import ShelfMixin, AnalysisPlugin
class MyPlugin(ShelfMixin, AnalysisPlugin):
def train(self):
''' Code to train the classifier
'''
# Here goes the code
# ...
return classifier
def activate(self):
if 'classifier' not in self.sh:
classifier = self.train()
self.sh['classifier'] = classifier
self.classifier = self.sh['classifier']
def deactivate(self):
self.close()
You can specify a 'shelf_file' in your .senpy file. By default the ShelfMixin creates a file based on the plugin name and stores it in that plugin's folder.
Shelves may get corrupted if the plugin exists unexpectedly.
A corrupt shelf prevents the plugin from loading.
If you do not care about the pickle, you can force your plugin to remove the corrupted file and load anyway, set the 'force_shelf' to True in your .senpy file.
How can I turn an external service into a plugin?
?????????????????????????????????????????????????
This example ilustrate how to implement a plugin that accesses the Sentiment140 service.
.. code:: python
class Sentiment140Plugin(SentimentPlugin):
def analyse_entry(self, entry, params):
text = entry.text
lang = params.get("language", "auto")
res = requests.post("http://www.sentiment140.com/api/bulkClassifyJson",
json.dumps({"language": lang,
"data": [{"text": text}]
}
)
)
p = params.get("prefix", None)
polarity_value = self.maxPolarityValue*int(res.json()["data"][0]
["polarity"]) * 0.25
polarity = "marl:Neutral"
neutral_value = self.maxPolarityValue / 2.0
if polarity_value > neutral_value:
polarity = "marl:Positive"
elif polarity_value < neutral_value:
polarity = "marl:Negative"
sentiment = Sentiment(id="Sentiment0",
prefix=p,
marl__hasPolarity=polarity,
marl__polarityValue=polarity_value)
sentiment.prov__wasGeneratedBy = self.id
entry.sentiments.append(sentiment)
yield entry
Can my plugin require additional parameters from the user?
??????????????????????????????????????????????????????????
You can add extra parameters in the definition file under the attribute ``extra_params``.
It takes a dictionary, where the keys are the name of the argument/parameter, and the value has the following fields:
* aliases: the different names which can be used in the request to use the parameter.
* required: if set to true, users need to provide this parameter unless a default is set.
* options: the different acceptable values of the parameter (i.e. an enum). If set, the value provided must match one of the options.
* default: the default value of the parameter, if none is provided in the request.
.. code:: python
extra_params
language:
aliases:
- language
- lang
- l
required: true,
options:
- es
- en
default: es
This example shows how to introduce a parameter associated with language.
The extraction of this paremeter is used in the analyse method of the Plugin interface.
.. code:: python
lang = params.get("language")
Where can I set up variables for using them in my plugin?
?????????????????????????????????????????????????????????
You can add these variables in the definition file with the structure of attribute-value pairs.
Every field added to the definition file is available to the plugin instance.
Can I activate a DEBUG mode for my plugin?
???????????????????????????????????????????
You can activate the DEBUG mode by the command-line tool using the option -d.
.. code:: bash
senpy -d
Additionally, with the ``--pdb`` option you will be dropped into a pdb post mortem shell if an exception is raised.
.. code:: bash
senpy --pdb
Where can I find more code examples?
????????????????????????????????????
See: `<http://github.com/gsi-upm/senpy-plugins-community>`_.

View File

@ -1,49 +0,0 @@
Projects using Senpy
--------------------
Are you using Senpy in your work?, we would love to hear from you!
Here is a list of on-going and past projects that have benefited from senpy:
MixedEmotions
,,,,,,,,,,,,,
`MixedEmotions <https://mixedemotions-project.eu/>`_ develops innovative multilingual multi-modal Big Data analytics applications.
The analytics relies on a common toolbox for multi-modal sentiment and emotion analysis.
The NLP parts of the toolbox are based on senpy and its API.
The toolbox is featured in this publication:
.. code-block:: text
Buitelaar, P., Wood, I. D., Arcan, M., McCrae, J. P., Abele, A., Robin, C., … Tummarello, G. (2018).
MixedEmotions: An Open-Source Toolbox for Multi-Modal Emotion Analysis.
IEEE Transactions on Multimedia.
EuroSentiment
,,,,,,,,,,,,,
The aim of the EUROSENTIMENT project was to create a pool for multilingual language resources and services for Sentiment Analysis.
The EuroSentiment project was the main motivation behind the development of Senpy, and some early versions were used:
.. code-block:: text
Sánchez-Rada, J. F., Vulcu, G., Iglesias, C. A., & Buitelaar, P. (2014).
EUROSENTIMENT: Linked Data Sentiment Analysis.
Proceedings of the ISWC 2014 Posters & Demonstrations Track
13th International Semantic Web Conference (ISWC 2014) (Vol. 1272, pp. 145148).
SoMeDi
,,,,,,
`SoMeDi <https://itea3.org/project/somedi.html>`_ is an ITEA3 project to research machine learning and artificial intelligence techniques that can be used to turn digital interaction data into Digital Interaction Intelligence and approaches that can be used to effectively enter and act in social media, and to automate this process.
SoMeDi exploits senpy's interoperability of services in their customizable data enrichment and NLP workflows.
TRIVALENT
,,,,,,,,,
`TRIVALENT <https://trivalent-project.eu/>`_ is an EU funded project which aims to a better understanding of root causes of the phenomenon of violent radicalisation in Europe in order to develop appropriate countermeasures, ranging from early detection methodologies to techniques of counter-narrative.
In addition to sentiment and emotion analysis services, trivalent provides other types of senpy services such as radicalism and writing style analysis.

View File

@ -1,36 +0,0 @@
Publications
============
And if you use Senpy in your research, please cite `Senpy: A Pragmatic Linked Sentiment Analysis Framework <http://gsi.upm.es/index.php/es/investigacion/publicaciones?view=publication&task=show&id=417>`__ (`BibTex <http://gsi.upm.es/index.php/es/investigacion/publicaciones?controller=publications&task=export&format=bibtex&id=417>`__):
.. code-block:: text
Sánchez-Rada, J. F., Iglesias, C. A., Corcuera, I., & Araque, Ó. (2016, October).
Senpy: A Pragmatic Linked Sentiment Analysis Framework.
In Data Science and Advanced Analytics (DSAA),
2016 IEEE International Conference on (pp. 735-742). IEEE.
Senpy uses Onyx for emotion representation, first introduced in:
.. code-block:: text
Sánchez-Rada, J. F., & Iglesias, C. A. (2016).
Onyx: A linked data approach to emotion representation.
Information Processing & Management, 52(1), 99-114.
Senpy uses Marl for sentiment representation, which was presented in:
.. code-block:: text
Westerski, A., Iglesias Fernandez, C. A., & Tapia Rico, F. (2011).
Linked opinions: Describing sentiments on the structured web of data.
The representation models, formats and challenges are partially covered in a chapter of the book Sentiment Analysis in Social Networks:
.. code-block:: text
Iglesias, C. A., Sánchez-Rada, J. F., Vulcu, G., & Buitelaar, P. (2017).
Linked Data Models for Sentiment and Emotion Analysis in Social Networks.
In Sentiment Analysis in Social Networks (pp. 49-69).

View File

@ -1,3 +1,2 @@
sphinxcontrib-httpdomain>=1.4
ipykernel
nbsphinx

View File

@ -1,27 +1,51 @@
What is Senpy?
--------------
Senpy is a framework for sentiment and emotion analysis services.
Its goal is to produce analysis services that are interchangeable and fully interoperable.
Web services can get really complex: data validation, user interaction, formatting, logging., etc.
The figure below summarizes the typical features in an analysis service.
Senpy implements all the common blocks, so developers can focus on what really matters: great analysis algorithms that solve real problems.
.. image:: senpy-framework.png
:width: 60%
:align: center
Senpy for end users
===================
All services built using senpy share a common interface.
This allows users to use them (almost) interchangeably.
Senpy comes with a :ref:`built-in client`.
Senpy for service developers
============================
Senpy is a framework that turns your sentiment or emotion analysis algorithm into a full blown semantic service.
Senpy takes care of:
* Interfacing with the user: parameter validation, error handling.
* Formatting: JSON-LD, Turtle/n-triples input and output, or simple text input
* Linked Data: senpy results are semantically annotated, using a series of well established vocabularies, and sane default URIs.
* User interface: a web UI where users can explore your service and test different settings
* A client to interact with the service. Currently only available in Python.
Sharing your sentiment analysis with the world has never been easier!
Check out the :doc:`plugins` if you have developed an analysis algorithm (e.g. sentiment analysis) and you want to publish it as a service.
Architecture
============
The main component of a sentiment analysis service is the algorithm itself. However, for the algorithm to work, it needs to get the appropriate parameters from the user, format the results according to the defined API, interact with the user whn errors occur or more information is needed, etc.
Senpy proposes a modular and dynamic architecture that allows:
* Implementing different algorithms in a extensible way, yet offering a common interface.
* Offering common services that facilitate development, so developers can focus on implementing new and better algorithms.
The framework consists of two main modules: Senpy core, which is the building block of the service, and Senpy plugins, which consist of the analysis algorithm. The next figure depicts a simplified version of the processes involved in an analysis with the Senpy framework.
.. image:: senpy-architecture.png
:width: 100%
:align: center
All services built using senpy share a common interface.
This allows users to use them (almost) interchangeably, with the same API and tools, simply by pointing to a different URL or changing a parameter.
The common schema also makes it easier to evaluate the performance of different algorithms and services.
In fact, Senpy has a built-in evaluation API you can use to compare results with different algorithms.
Services can also use the common interface to communicate with each other.
And higher level features can be built on top of these services, such as automatic fusion of results, emotion model conversion, and service discovery.
These benefits are not limited to new services.
The community has developed wrappers for some proprietary and commercial services (such as sentiment140 and Meaning Cloud), so you can consult them as.
Senpy comes with a built-in client in the client package.
To achieve this goal, Senpy uses a Linked Data principled approach, based on the NIF (NLP Interchange Format) specification, and open vocabularies such as Marl and Onyx.
You can learn more about this in :doc:`vocabularies`.
Check out :doc:`development` if you have developed an analysis algorithm (e.g. sentiment analysis) and you want to publish it as a service.

View File

@ -1,85 +0,0 @@
Command line tool
=================
Basic usage
-----------
The senpy server is launched via the `senpy` command:
.. code:: text
usage: senpy [-h] [--level logging_level] [--log-format log_format] [--debug]
[--no-default-plugins] [--host HOST] [--port PORT]
[--plugins-folder PLUGINS_FOLDER] [--install]
[--test] [--no-run] [--data-folder DATA_FOLDER]
[--no-threaded] [--no-deps] [--version] [--allow-fail]
Run a Senpy server
optional arguments:
-h, --help show this help message and exit
--level logging_level, -l logging_level
Logging level
--log-format log_format
Logging format
--debug, -d Run the application in debug mode
--no-default-plugins Do not load the default plugins
--host HOST Use 0.0.0.0 to accept requests from any host.
--port PORT, -p PORT Port to listen on.
--plugins-folder PLUGINS_FOLDER, -f PLUGINS_FOLDER
Where to look for plugins.
--install, -i Install plugin dependencies before launching the server.
--test, -t Test all plugins before launching the server
--no-run Do not launch the server
--data-folder DATA_FOLDER, --data DATA_FOLDER
Where to look for data. It be set with the SENPY_DATA
environment variable as well.
--no-threaded Run the server without threading
--no-deps, -n Skip installing dependencies
--version, -v Output the senpy version and exit
--allow-fail, --fail Do not exit if some plugins fail to activate
When launched, the server will recursively look for plugins in the specified plugins folder (the current working directory by default).
For every plugin found, it will download its dependencies, and try to activate it.
The default server includes a playground and an endpoint with all plugins found.
Let's run senpy with the default plugins:
.. code:: bash
senpy -f .
Now open your browser and go to `http://localhost:5000 <http://localhost:5000>`_, where you should be greeted by the senpy playground:
.. image:: senpy-playground.png
:width: 100%
:alt: Playground
The playground is a user-friendly way to test your plugins, but you can always use the service directly: `http://localhost:5000/api?input=hello <http://localhost:5000/api?input=hello>`_.
By default, senpy will listen only on `127.0.0.1`.
That means you can only access the API from your PC (i.e. localhost).
You can listen on a different address using the `--host` flag (e.g., 0.0.0.0, to allow any computer to access it).
The default port is 5000.
You can change it with the `--port` flag.
For instance, to accept connections on port 6000 on any interface:
.. code:: bash
senpy --host 0.0.0.0 --port 6000
For more options, see the `--help` page.
Sentiment analysis in the command line
--------------------------------------
Although the main use of senpy is to publish services, the tool can also be used locally to analyze text in the command line.
This is a short video demonstration:
.. image:: https://asciinema.org/a/9uwef1ghkjk062cw2t4mhzpyk.png
:width: 100%
:target: https://asciinema.org/a/9uwef1ghkjk062cw2t4mhzpyk
:alt: CLI demo

58
docs/server.rst Normal file
View File

@ -0,0 +1,58 @@
Server
======
The senpy server is launched via the `senpy` command:
.. code:: text
usage: senpy [-h] [--level logging_level] [--debug] [--default-plugins]
[--host HOST] [--port PORT] [--plugins-folder PLUGINS_FOLDER]
[--only-install]
Run a Senpy server
optional arguments:
-h, --help show this help message and exit
--level logging_level, -l logging_level
Logging level
--debug, -d Run the application in debug mode
--default-plugins Load the default plugins
--host HOST Use 0.0.0.0 to accept requests from any host.
--port PORT, -p PORT Port to listen on.
--plugins-folder PLUGINS_FOLDER, -f PLUGINS_FOLDER
Where to look for plugins.
--only-install, -i Do not run a server, only install plugin dependencies
When launched, the server will recursively look for plugins in the specified plugins folder (the current working directory by default).
For every plugin found, it will download its dependencies, and try to activate it.
The default server includes a playground and an endpoint with all plugins found.
Let's run senpy with the default plugins:
.. code:: bash
senpy -f . --default-plugins
Now go to `http://localhost:5000 <http://localhost:5000>`_, you should be greeted by the senpy playground:
.. image:: senpy-playground.png
:width: 100%
:alt: Playground
The playground is a user-friendly way to test your plugins, but you can always use the service directly: `http://localhost:5000/api?input=hello <http://localhost:5000/api?input=hello>`_.
By default, senpy will listen only on the `127.0.0.1` address.
That means you can only access the API from your (or localhost).
You can listen on a different address using the `--host` flag (e.g., 0.0.0.0).
The default port is 5000.
You can change it with the `--port` flag.
For instance, to accept connections on port 6000 on any interface:
.. code:: bash
senpy --host 0.0.0.0 --port 6000
For more options, see the `--help` page.

15
docs/usage.rst Normal file
View File

@ -0,0 +1,15 @@
Usage
-----
First of all, you need to install the package.
See :doc:`installation` for instructions.
Once installed, the `senpy` command should be available.
.. toctree::
:maxdepth: 1
server
SenpyClientUse
commandline

View File

@ -1,24 +1,8 @@
Vocabularies and model
======================
The model used in Senpy is based on NIF 2.0 [1], which defines a semantic format and API for improving interoperability among natural language processing services.
The model used in Senpy is based on the following vocabularies:
Senpy has been applied to sentiment and emotion analysis services using the following vocabularies:
* Marl [2,6], a vocabulary designed to annotate and describe subjetive opinions expressed on the web or in information systems.
* Onyx [3,5], which is built one the same principles as Marl to annotate and describe emotions, and provides interoperability with Emotion Markup Language.
An overview of the vocabularies and their use can be found in [4].
[1] Guidelines for developing NIF-based NLP services, Final Community Group Report 22 December 2015 Available at: https://www.w3.org/2015/09/bpmlod-reports/nif-based-nlp-webservices/
[2] Marl Ontology Specification, available at http://www.gsi.upm.es/ontologies/marl/
[3] Onyx Ontology Specification, available at http://www.gsi.upm.es/ontologies/onyx/
[4] Iglesias, C. A., Sánchez-Rada, J. F., Vulcu, G., & Buitelaar, P. (2017). Linked Data Models for Sentiment and Emotion Analysis in Social Networks. In Sentiment Analysis in Social Networks (pp. 49-69).
[5] Sánchez-Rada, J. F., & Iglesias, C. A. (2016). Onyx: A linked data approach to emotion representation. Information Processing & Management, 52(1), 99-114.
[6] Westerski, A., Iglesias Fernandez, C. A., & Tapia Rico, F. (2011). Linked opinions: Describing sentiments on the structured web of data.
* Marl, a vocabulary designed to annotate and describe subjetive opinions expressed on the web or in information systems.
* Onyx, which is built one the same principles as Marl to annotate and describe emotions, and provides interoperability with Emotion Markup Language.
* NIF 2.0, which defines a semantic format and APO for improving interoperability among natural language processing services

View File

@ -1,23 +0,0 @@
This is a collection of plugins that exemplify certain aspects of plugin development with senpy.
The first series of plugins are the `basic` ones.
Their starting point is a classification function defined in `basic.py`.
They all include testing and running them as a script will run all tests.
In ascending order of customization, the plugins are:
* Basic is the simplest plugin of all. It leverages the `SentimentBox` Plugin class to create a plugin out of a classification method, and `MappingMixin` to convert the labels from (`pos`, `neg`) to (`marl:Positive`, `marl:Negative`
* Basic_box is just like the previous one, but replaces the mixin with a custom function.
* Basic_configurable is a version of `basic` with a configurable map of emojis for each sentiment.
* Basic_parameterized like `basic_info`, but users set the map in each query (via `extra_parameters`).
* Basic_analyse\_entry uses the more general `analyse_entry` method and adds the annotations individually.
In rest of the plugins show advanced topics:
* mynoop: shows how to add a definition file with external requirements for a plugin. Doing this with a python-only module would require moving all imports of the requirements to their functions, which is considered bad practice.
* Async: a barebones example of training a plugin and analyzing data in parallel.
All of the plugins in this folder include a set of test cases and they are periodically tested with the latest version of senpy.
Additioanlly, for an example of stand-alone plugin that can be tested and deployed with docker, take a look at: lab.gsi.upm.es/senpy/plugin-example
bbm

View File

@ -1,53 +0,0 @@
#
# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://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.
#
from senpy import AnalysisPlugin
import multiprocessing
def _train(process_number):
return process_number
class Async(AnalysisPlugin):
'''An example of an asynchronous module'''
author = '@balkian'
version = '0.2'
sync = False
def _do_async(self, num_processes):
pool = multiprocessing.Pool(processes=num_processes)
values = sorted(pool.map(_train, range(num_processes)))
return values
def activate(self):
self.value = self._do_async(4)
def analyse_entry(self, entry, params):
values = self._do_async(2)
entry.async_values = values
yield entry
test_cases = [
{
'input': 'any',
'expected': {
'async_values': [0, 1]
}
}
]

View File

@ -1,41 +0,0 @@
#!/usr/local/bin/python
# -*- coding: utf-8 -*-
#
# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://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.
#
emoticons = {
'pos': [':)', ':]', '=)', ':D'],
'neg': [':(', ':[', '=(']
}
emojis = {
'pos': [u'😁', u'😂', u'😃', u'😄', u'😆', u'😅', u'😄', u'😍'],
'neg': [u'😢', u'😡', u'😠', u'😞', u'😖', u'😔', u'😓', u'😒']
}
def get_polarity(text, dictionaries=[emoticons, emojis]):
polarity = 'marl:Neutral'
print('Input for get_polarity', text)
for dictionary in dictionaries:
for label, values in dictionary.items():
for emoticon in values:
if emoticon and emoticon in text:
polarity = label
break
print('Polarity', polarity)
return polarity

View File

@ -1,62 +0,0 @@
#!/usr/local/bin/python
# -*- coding: utf-8 -*-
#
# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://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.
#
from senpy import easy_test, models, plugins
import basic
class BasicAnalyseEntry(plugins.SentimentPlugin):
'''Equivalent to Basic, implementing the analyse_entry method'''
author = '@balkian'
version = '0.1'
mappings = {
'pos': 'marl:Positive',
'neg': 'marl:Negative',
'default': 'marl:Neutral'
}
def analyse_entry(self, entry, activity):
polarity = basic.get_polarity(entry.text)
polarity = self.mappings.get(polarity, self.mappings['default'])
s = models.Sentiment(marl__hasPolarity=polarity)
s.prov(activity)
entry.sentiments.append(s)
yield entry
test_cases = [{
'input': 'Hello :)',
'polarity': 'marl:Positive'
}, {
'input': 'So sad :(',
'polarity': 'marl:Negative'
}, {
'input': 'Yay! Emojis 😁',
'polarity': 'marl:Positive'
}, {
'input': 'But no emoticons 😢',
'polarity': 'marl:Negative'
}]
if __name__ == '__main__':
easy_test()

View File

@ -1,54 +0,0 @@
#!/usr/local/bin/python
# -*- coding: utf-8 -*-
#
# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://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.
#
from senpy import easy_test, SentimentBox
import basic
class BasicBox(SentimentBox):
''' A modified version of Basic that also does converts annotations manually'''
author = '@balkian'
version = '0.1'
def predict_one(self, features, **kwargs):
output = basic.get_polarity(features[0])
if output == 'pos':
return [1, 0, 0]
if output == 'neg':
return [0, 0, 1]
return [0, 1, 0]
test_cases = [{
'input': 'Hello :)',
'polarity': 'marl:Positive'
}, {
'input': 'So sad :(',
'polarity': 'marl:Negative'
}, {
'input': 'Yay! Emojis 😁',
'polarity': 'marl:Positive'
}, {
'input': 'But no emoticons 😢',
'polarity': 'marl:Negative'
}]
if __name__ == '__main__':
easy_test()

View File

@ -1,55 +0,0 @@
#!/usr/local/bin/python
# -*- coding: utf-8 -*-
#
# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://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.
#
from senpy import easy_test, SentimentBox
import basic
class Basic(SentimentBox):
'''Provides sentiment annotation using a lexicon'''
author = '@balkian'
version = '0.1'
def predict_one(self, features, **kwargs):
output = basic.get_polarity(features[0])
if output == 'pos':
return [1, 0, 0]
if output == 'neu':
return [0, 1, 0]
return [0, 0, 1]
test_cases = [{
'input': u'Hello :)',
'polarity': 'marl:Positive'
}, {
'input': u'So sad :(',
'polarity': 'marl:Negative'
}, {
'input': u'Yay! Emojis 😁',
'polarity': 'marl:Positive'
}, {
'input': u'But no emoticons 😢',
'polarity': 'marl:Negative'
}]
if __name__ == '__main__':
easy_test()

View File

@ -1,121 +0,0 @@
#!/usr/local/bin/python
# -*- coding: utf-8 -*-
#
# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://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.
#
from senpy import easy_test, models, plugins
import basic
class Dictionary(plugins.SentimentPlugin):
'''Sentiment annotation using a configurable lexicon'''
author = '@balkian'
version = '0.2'
dictionaries = [basic.emojis, basic.emoticons]
mappings = {'pos': 'marl:Positive', 'neg': 'marl:Negative'}
def analyse_entry(self, entry, *args, **kwargs):
polarity = basic.get_polarity(entry.text, self.dictionaries)
if polarity in self.mappings:
polarity = self.mappings[polarity]
s = models.Sentiment(marl__hasPolarity=polarity)
s.prov(self)
entry.sentiments.append(s)
yield entry
test_cases = [{
'input': 'Hello :)',
'polarity': 'marl:Positive'
}, {
'input': 'So sad :(',
'polarity': 'marl:Negative'
}, {
'input': 'Yay! Emojis 😁',
'polarity': 'marl:Positive'
}, {
'input': 'But no emoticons 😢',
'polarity': 'marl:Negative'
}]
class EmojiOnly(Dictionary):
'''Sentiment annotation with a basic lexicon of emojis'''
dictionaries = [basic.emojis]
test_cases = [{
'input': 'Hello :)',
'polarity': 'marl:Neutral'
}, {
'input': 'So sad :(',
'polarity': 'marl:Neutral'
}, {
'input': 'Yay! Emojis 😁',
'polarity': 'marl:Positive'
}, {
'input': 'But no emoticons 😢',
'polarity': 'marl:Negative'
}]
class EmoticonsOnly(Dictionary):
'''Sentiment annotation with a basic lexicon of emoticons'''
dictionaries = [basic.emoticons]
test_cases = [{
'input': 'Hello :)',
'polarity': 'marl:Positive'
}, {
'input': 'So sad :(',
'polarity': 'marl:Negative'
}, {
'input': 'Yay! Emojis 😁',
'polarity': 'marl:Neutral'
}, {
'input': 'But no emoticons 😢',
'polarity': 'marl:Neutral'
}]
class Salutes(Dictionary):
'''Sentiment annotation with a custom lexicon, for illustration purposes'''
dictionaries = [{
'marl:Positive': ['Hello', '!'],
'marl:Negative': ['Good bye', ]
}]
test_cases = [{
'input': 'Hello :)',
'polarity': 'marl:Positive'
}, {
'input': 'Good bye :(',
'polarity': 'marl:Negative'
}, {
'input': 'Yay! Emojis 😁',
'polarity': 'marl:Positive'
}, {
'input': 'But no emoticons 😢',
'polarity': 'marl:Neutral'
}]
if __name__ == '__main__':
easy_test()

View File

@ -1,41 +0,0 @@
#
# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://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.
#
from senpy import AnalysisPlugin, easy
class Dummy(AnalysisPlugin):
'''This is a dummy self-contained plugin'''
author = '@balkian'
version = '0.1'
def analyse_entry(self, entry, params):
entry['nif:isString'] = entry['nif:isString'][::-1]
entry.reversed = entry.get('reversed', 0) + 1
yield entry
test_cases = [{
'entry': {
'nif:isString': 'Hello',
},
'expected': {
'nif:isString': 'olleH'
}
}]
if __name__ == '__main__':
easy()

View File

@ -1,56 +0,0 @@
#
# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://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.
#
from senpy import AnalysisPlugin, easy
class DummyRequired(AnalysisPlugin):
'''This is a dummy self-contained plugin'''
author = '@balkian'
version = '0.1'
extra_params = {
'example': {
'description': 'An example parameter',
'required': True,
'options': ['a', 'b']
}
}
def analyse_entry(self, entry, params):
entry['nif:isString'] = entry['nif:isString'][::-1]
entry.reversed = entry.get('reversed', 0) + 1
yield entry
test_cases = [{
'entry': {
'nif:isString': 'Hello',
},
'should_fail': True
}, {
'entry': {
'nif:isString': 'Hello',
},
'params': {
'example': 'a'
},
'expected': {
'nif:isString': 'olleH'
}
}]
if __name__ == '__main__':
easy()

View File

@ -1,49 +0,0 @@
#
# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://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.
#
import random
from senpy.plugins import EmotionPlugin
from senpy.models import EmotionSet, Emotion, Entry
class EmoRand(EmotionPlugin):
'''A sample plugin that returns a random emotion annotation'''
name = 'emotion-random'
author = '@balkian'
version = '0.1'
url = "https://github.com/gsi-upm/senpy-plugins-community"
onyx__usesEmotionModel = "emoml:big6"
def analyse_entry(self, entry, activity):
category = "emoml:big6happiness"
number = max(-1, min(1, random.gauss(0, 0.5)))
if number > 0:
category = "emoml:big6anger"
emotionSet = EmotionSet()
emotion = Emotion({"onyx:hasEmotionCategory": category})
emotionSet.onyx__hasEmotion.append(emotion)
emotionSet.prov(activity)
entry.emotions.append(emotionSet)
yield entry
def test(self):
params = dict()
results = list()
for i in range(100):
res = next(self.analyse_entry(Entry(nif__isString="Hello"), self.activity(params)))
res.validate()
results.append(res.emotions[0]['onyx:hasEmotion'][0]['onyx:hasEmotionCategory'])

View File

@ -1,40 +0,0 @@
#
# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://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.
#
import noop
from senpy.plugins import SentimentPlugin
class NoOp(SentimentPlugin):
'''This plugin does nothing. Literally nothing.'''
version = 0
def analyse_entry(self, entry, *args, **kwargs):
yield entry
def test(self):
print(dir(noop))
super(NoOp, self).test()
test_cases = [{
'entry': {
'nif:isString': 'hello'
},
'expected': {
'nif:isString': 'hello'
}
}]

View File

@ -1,4 +0,0 @@
module: mynoop
optional: true
requirements:
- noop

View File

@ -1,80 +0,0 @@
#!/usr/local/bin/python
# -*- coding: utf-8 -*-
#
# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://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.
#
from senpy import easy_test, models, plugins
import basic
class ParameterizedDictionary(plugins.SentimentPlugin):
'''This is a basic self-contained plugin'''
author = '@balkian'
version = '0.2'
extra_params = {
'positive-words': {
'description': 'Comma-separated list of words that are considered positive',
'aliases': ['positive'],
'required': True
},
'negative-words': {
'description': 'Comma-separated list of words that are considered negative',
'aliases': ['negative'],
'required': False
}
}
def analyse_entry(self, entry, activity):
params = activity.params
positive_words = params['positive-words'].split(',')
negative_words = params['negative-words'].split(',')
dictionary = {
'marl:Positive': positive_words,
'marl:Negative': negative_words,
}
polarity = basic.get_polarity(entry.text, [dictionary])
s = models.Sentiment(marl__hasPolarity=polarity)
s.prov(activity)
entry.sentiments.append(s)
yield entry
test_cases = [
{
'input': 'Hello :)',
'polarity': 'marl:Positive',
'parameters': {
'positive': "Hello,:)",
'negative': "sad,:()"
}
},
{
'input': 'Hello :)',
'polarity': 'marl:Negative',
'parameters': {
'positive': "",
'negative': "Hello"
}
}
]
if __name__ == '__main__':
easy_test()

View File

@ -1,54 +0,0 @@
#
# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://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.
#
import random
from senpy import SentimentPlugin, Sentiment, Entry
class RandSent(SentimentPlugin):
'''A sample plugin that returns a random sentiment annotation'''
name = 'sentiment-random'
author = "@balkian"
version = '0.1'
url = "https://github.com/gsi-upm/senpy-plugins-community"
marl__maxPolarityValue = '1'
marl__minPolarityValue = "-1"
def analyse_entry(self, entry, activity):
polarity_value = max(-1, min(1, random.gauss(0.2, 0.2)))
polarity = "marl:Neutral"
if polarity_value > 0:
polarity = "marl:Positive"
elif polarity_value < 0:
polarity = "marl:Negative"
sentiment = Sentiment(marl__hasPolarity=polarity,
marl__polarityValue=polarity_value)
sentiment.prov(activity)
entry.sentiments.append(sentiment)
yield entry
def test(self):
'''Run several random analyses.'''
params = dict()
results = list()
for i in range(50):
activity = self.activity(params)
res = next(self.analyse_entry(Entry(nif__isString="Hello"),
activity))
res.validate()
results.append(res.sentiments[0]['marl:hasPolarity'])
assert 'marl:Positive' in results
assert 'marl:Negative' in results

View File

@ -1,49 +0,0 @@
#
# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://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.
#
'''
Create a dummy dataset.
Messages with a happy emoticon are labelled positive
Messages with a sad emoticon are labelled negative
'''
import random
dataset = []
vocabulary = ['hello', 'world', 'senpy', 'cool', 'goodbye', 'random', 'text']
emojimap = {
1: [':)', ],
-1: [':(', ]
}
for tag, values in emojimap.items():
for i in range(1000):
msg = ''
for j in range(3):
msg += random.choice(vocabulary)
msg += " "
msg += random.choice(values)
dataset.append([msg, tag])
text = []
labels = []
for i in dataset:
text.append(i[0])
labels.append(i[1])

View File

@ -1,46 +0,0 @@
#
# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://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.
#
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from mydata import text, labels
X_train, X_test, y_train, y_test = train_test_split(text, labels, test_size=0.12, random_state=42)
from sklearn.naive_bayes import MultinomialNB
count_vec = CountVectorizer(tokenizer=lambda x: x.split())
clf3 = MultinomialNB()
pipeline = Pipeline([('cv', count_vec),
('clf', clf3)])
pipeline.fit(X_train, y_train)
print('Feature names: {}'.format(count_vec.get_feature_names_out()))
print('Class count: {}'.format(clf3.class_count_))
if __name__ == '__main__':
print('--Results--')
tests = [
(['The sentiment for senpy should be positive :)', ], 1),
(['The sentiment for anything else should be negative :()', ], -1)
]
for features, expected in tests:
result = pipeline.predict(features)
print('Input: {}\nExpected: {}\nGot: {}'.format(features[0], expected, result))

View File

@ -1,48 +0,0 @@
#
# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://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.
#
from senpy import SentimentBox, easy_test
from mypipeline import pipeline
class PipelineSentiment(SentimentBox):
'''This is a pipeline plugin that wraps a classifier defined in another module
(mypipeline).'''
author = '@balkian'
version = 0.1
maxPolarityValue = 1
minPolarityValue = -1
def predict_one(self, features, **kwargs):
if pipeline.predict(features) > 0:
return [1, 0, 0]
return [0, 0, 1]
test_cases = [
{
'input': 'The sentiment for senpy should be positive :)',
'polarity': 'marl:Positive'
},
{
'input': 'The sentiment for senpy should be negative :(',
'polarity': 'marl:Negative'
}
]
if __name__ == '__main__':
easy_test()

View File

@ -1,43 +0,0 @@
#
# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://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.
#
from senpy.plugins import AnalysisPlugin
from time import sleep
class Sleep(AnalysisPlugin):
'''Dummy plugin to test async'''
author = "@balkian"
version = "0.2"
timeout = 0.05
extra_params = {
"timeout": {
"@id": "timeout_sleep",
"aliases": ["timeout", "to"],
"required": False,
"default": 0
}
}
def activate(self, *args, **kwargs):
sleep(self.timeout)
def analyse_entry(self, entry, params):
sleep(float(params.get("timeout", self.timeout)))
yield entry
def test(self):
pass

View File

@ -1,6 +0,0 @@
gsitk>0.1.9.1
flask_cors==3.0.10
Pattern==3.6
lxml==4.9.3
pandas==2.1.1
textblob==0.17.1

View File

@ -1,24 +1,22 @@
---
apiVersion: apps/v1
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
name: senpy-latest
spec:
replicas: 1
selector:
matchLabels:
app: senpy-latest
template:
metadata:
labels:
app: senpy-latest
role: senpy-latest
app: test
spec:
containers:
- name: senpy-latest
image: $IMAGEWTAG
image: gsiupm/senpy:latest
imagePullPolicy: Always
args: ["--enable-cors"]
args:
- "--default-plugins"
resources:
limits:
memory: "512Mi"
@ -26,11 +24,3 @@ spec:
ports:
- name: web
containerPort: 5000
volumeMounts:
- name: senpy-data
mountPath: /senpy-data
subPath: data
volumes:
- name: senpy-data
persistentVolumeClaim:
claimName: pvc-senpy

View File

@ -1,29 +1,14 @@
---
apiVersion: networking.k8s.io/v1
apiVersion: extensions/v1beta1
kind: Ingress
metadata:
name: senpy-ingress
labels:
app: senpy-latest
spec:
rules:
- host: senpy-latest.gsi.upm.es
- host: latest.senpy.cluster.gsi.dit.upm.es
http:
paths:
- path: /
pathType: Prefix
backend:
service:
name: senpy-latest
port:
number: 5000
- host: latest.senpy.gsi.upm.es
http:
paths:
- path: /
pathType: Prefix
backend:
service:
name: senpy-latest
port:
number: 5000
serviceName: senpy-latest
servicePort: 5000

View File

@ -3,12 +3,10 @@ apiVersion: v1
kind: Service
metadata:
name: senpy-latest
labels:
app: senpy-latest
spec:
type: ClusterIP
ports:
- port: 5000
protocol: TCP
selector:
app: senpy-latest
role: senpy-latest

View File

@ -3,13 +3,10 @@ requests>=2.4.1
tornado>=4.4.3
PyLD>=0.6.5
nltk
six
future
jsonschema
jsonref
PyYAML
rdflib==6.1.1
numpy
scipy
scikit-learn>=0.20
responses
jmespath
rdflib
rdflib-jsonld

View File

@ -1,7 +1,7 @@
#!/usr/bin/python
# -*- coding: utf-8 -*-
#
# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
# Copyright 2014 J. Fernando Sánchez Rada - Grupo de Sistemas Inteligentes
# DIT, UPM
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -14,7 +14,6 @@
# 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.
#
"""
Sentiment analysis server in Python
"""
@ -26,10 +25,4 @@ logger = logging.getLogger(__name__)
logger.info('Using senpy version: {}'.format(__version__))
from .utils import easy, easy_load, easy_test # noqa: F401
from .models import * # noqa: F401,F403
from .plugins import * # noqa: F401,F403
from .extensions import * # noqa: F401,F403
__all__ = ['api', 'blueprints', 'cli', 'extensions', 'models', 'plugins']

View File

@ -1,6 +1,7 @@
#!/usr/bin/python
# -*- coding: utf-8 -*-
# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
# Copyright 2014 J. Fernando Sánchez Rada - Grupo de Sistemas Inteligentes
# DIT, UPM
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -21,9 +22,6 @@ the server.
from flask import Flask
from senpy.extensions import Senpy
from senpy.utils import easy_test
from senpy.plugins import list_dependencies
from senpy import config
import logging
import os
@ -43,17 +41,6 @@ def main():
type=str,
default="INFO",
help='Logging level')
parser.add_argument(
'--no-proxy-fix',
action='store_true',
default=False,
help='Do not assume senpy will be running behind a proxy (e.g., nginx)')
parser.add_argument(
'--log-format',
metavar='log_format',
type=str,
default='%(asctime)s %(levelname)-10s %(name)-30s \t %(message)s',
help='Logging format')
parser.add_argument(
'--debug',
'-d',
@ -61,10 +48,10 @@ def main():
default=False,
help='Run the application in debug mode')
parser.add_argument(
'--no-default-plugins',
'--default-plugins',
action='store_true',
default=False,
help='Do not load the default plugins')
help='Load the default plugins')
parser.add_argument(
'--host',
type=str,
@ -80,35 +67,14 @@ def main():
'--plugins-folder',
'-f',
type=str,
action='append',
default='plugins',
help='Where to look for plugins.')
parser.add_argument(
'--install',
'--only-install',
'-i',
action='store_true',
default=False,
help='Install plugin dependencies before running.')
parser.add_argument(
'--dependencies',
action='store_true',
default=False,
help='List plugin dependencies')
parser.add_argument(
'--strict',
action='store_true',
default=config.strict,
help='Fail if optional plugins cannot be loaded.')
parser.add_argument(
'--test',
'-t',
action='store_true',
default=False,
help='Test all plugins before launching the server')
parser.add_argument(
'--no-run',
action='store_true',
default=False,
help='Do not launch the server.')
help='Do not run a server, only install plugin dependencies')
parser.add_argument(
'--data-folder',
'--data',
@ -116,128 +82,40 @@ def main():
default=None,
help='Where to look for data. It be set with the SENPY_DATA environment variable as well.')
parser.add_argument(
'--no-threaded',
action='store_true',
default=False,
help='Run a single-threaded server')
parser.add_argument(
'--no-deps',
'-n',
action='store_true',
default=False,
help='Skip installing dependencies')
'--threaded',
action='store_false',
default=True,
help='Run a threaded server')
parser.add_argument(
'--version',
'-v',
action='store_true',
default=False,
help='Output the senpy version and exit')
parser.add_argument(
'--allow-fail',
'--fail',
action='store_true',
default=False,
help='Do not exit if some plugins fail to activate')
parser.add_argument(
'--enable-cors',
'--cors',
action='store_true',
default=False,
help='Enable CORS for all domains (requires flask-cors to be installed)')
args = parser.parse_args()
if args.version:
print('Senpy version {}'.format(senpy.__version__))
print(sys.version)
if args.version:
exit(1)
logging.basicConfig()
rl = logging.getLogger()
rl.setLevel(getattr(logging, args.level))
logger_handler = rl.handlers[0]
# First, generic formatter:
logger_handler.setFormatter(logging.Formatter(args.log_format))
app = Flask(__name__)
app.debug = args.debug
sp = Senpy(app,
plugin_folder=None,
default_plugins=not args.no_default_plugins,
install=args.install,
strict=args.strict,
sp = Senpy(app, args.plugins_folder,
default_plugins=args.default_plugins,
data_folder=args.data_folder)
folders = list(args.plugins_folder) if args.plugins_folder else []
if not folders:
folders.append(".")
for p in folders:
sp.add_folder(p)
plugins = sp.plugins(plugin_type=None, is_activated=False)
maxname = max(len(x.name) for x in plugins)
maxversion = max(len(str(x.version)) for x in plugins)
print('Found {} plugins:'.format(len(plugins)))
for plugin in plugins:
import inspect
fpath = inspect.getfile(plugin.__class__)
print('\t{: <{maxname}} @ {: <{maxversion}} -> {}'.format(plugin.name,
plugin.version,
fpath,
maxname=maxname,
maxversion=maxversion))
if args.dependencies:
print('Listing dependencies')
missing = []
installed = []
for plug in sp.plugins(is_activated=False):
inst, miss, nltkres = list_dependencies(plug)
if not any([inst, miss, nltkres]):
continue
print(f'Plugin: {plug.id}')
for m in miss:
missing.append(f'{m} # {plug.id}')
for i in inst:
installed.append(f'{i} # {plug.id}')
if installed:
print('Installed packages:')
for i in installed:
print(f'\t{i}')
if missing:
print('Missing packages:')
for m in missing:
print(f'\t{m}')
if args.install:
sp.install_deps()
if args.test:
sp.activate_all(sync=True)
easy_test(sp.plugins(is_activated=True), debug=args.debug)
if args.no_run:
if args.only_install:
return
sp.activate_all(sync=True)
if sp.strict:
inactive = sp.plugins(is_activated=False)
assert not inactive
sp.activate_all()
print('Senpy version {}'.format(senpy.__version__))
print('Server running on port %s:%d. Ctrl+C to quit' % (args.host,
args.port))
if args.enable_cors:
from flask_cors import CORS
CORS(app)
if not args.no_proxy_fix:
from werkzeug.middleware.proxy_fix import ProxyFix
app.wsgi_app = ProxyFix(app.wsgi_app)
try:
app.run(args.host,
args.port,
threaded=not args.no_threaded,
threaded=args.threaded,
debug=app.debug)
except KeyboardInterrupt:
print('Bye!')
sp.deactivate_all()

View File

@ -1,208 +1,76 @@
#
# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://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.
#
from future.utils import iteritems
from .models import Error, Results, Entry, from_string
import logging
logger = logging.getLogger(__name__)
boolean = [True, False]
processors = {
'string_to_tuple': lambda p: p if isinstance(p, (tuple, list)) else tuple(p.split(','))
}
API_PARAMS = {
"algorithm": {
"aliases": ["algorithms", "a", "algo"],
"required": True,
"default": 'default',
"processor": 'string_to_tuple',
"required": False,
"description": ("Algorithms that will be used to process the request."
"It may be a list of comma-separated names."),
},
"expanded-jsonld": {
"@id": "expanded-jsonld",
"description": "use JSON-LD expansion to get full URIs",
"aliases": ["expanded", "expanded_jsonld"],
"options": boolean,
"aliases": ["expanded"],
"required": True,
"default": False
"default": 0
},
"with-parameters": {
"with_parameters": {
"aliases": ['withparameters',
'with_parameters'],
"description": "include initial parameters in the response",
"options": boolean,
'with-parameters'],
"options": "boolean",
"default": False,
"required": True
},
"plugin_type": {
"@id": "pluginType",
"description": 'What kind of plugins to list',
"aliases": ["pluginType"],
"required": True,
"default": "analysisPlugin"
},
"outformat": {
"@id": "outformat",
"aliases": ["o"],
"default": "json-ld",
"description": """The data can be semantically formatted (JSON-LD, turtle or n-triples),
given as a list of comma-separated fields (see the fields option) or constructed from a Jinja2
template (see the template option).""",
"required": True,
"options": ["json-ld", "turtle", "ntriples"],
},
"template": {
"@id": "template",
"required": False,
"description": """Jinja2 template for the result. The input data for the template will
be the results as a dictionary.
For example:
Consider the results before templating:
```
[{
"@type": "entry",
"onyx:hasEmotionSet": [],
"nif:isString": "testing the template",
"marl:hasOpinion": [
{
"@type": "sentiment",
"marl:hasPolarity": "marl:Positive"
}
]
}]
```
And the template:
```
{% for entry in entries %}
{{ entry["nif:isString"] | upper }},{{entry.sentiments[0]["marl:hasPolarity"].split(":")[1]}}
{% endfor %}
```
The final result would be:
```
TESTING THE TEMPLATE,Positive
```
"""
},
"fields": {
"@id": "fields",
"required": False,
"description": """A jmespath selector, that can be used to extract a new dictionary, array or value
from the results.
jmespath is a powerful query language for json and/or dictionaries.
It allows you to change the structure (and data) of your objects through queries.
e.g., the following expression gets a list of `[emotion label, intensity]` for each entry:
`entries[]."onyx:hasEmotionSet"[]."onyx:hasEmotion"[]["onyx:hasEmotionCategory","onyx:hasEmotionIntensity"]`
For more information, see: https://jmespath.org
"""
"options": ["json-ld", "turtle"],
},
"help": {
"@id": "help",
"description": "Show additional help to know more about the possible parameters",
"aliases": ["h"],
"required": True,
"options": boolean,
"options": "boolean",
"default": False
},
"verbose": {
"@id": "verbose",
"description": "Show all properties in the result",
"aliases": ["v"],
"required": True,
"options": boolean,
"default": False
},
"aliases": {
"@id": "aliases",
"description": "Replace JSON properties with their aliases",
"aliases": [],
"required": True,
"options": boolean,
"default": False
},
"emotion-model": {
"emotionModel": {
"@id": "emotionModel",
"description": """Emotion model to use in the response.
Senpy will try to convert the output to this model automatically.
Examples: `wna:liking` and `emoml:big6`.
""",
"aliases": ["emoModel", "emotionModel"],
"aliases": ["emoModel"],
"required": False
},
"conversion": {
"@id": "conversion",
"description": """How to show the elements that have (not) been converted.
* full: converted and original elements will appear side-by-side
* filtered: only converted elements will be shown
* nested: converted elements will be shown, and they will include a link to the original element
(using `prov:wasGeneratedBy`).
""",
"description": "How to show the elements that have (not) been converted",
"required": True,
"options": ["filtered", "nested", "full"],
"default": "full"
}
}
EVAL_PARAMS = {
"algorithm": {
"aliases": ["plug", "p", "plugins", "algorithms", 'algo', 'a', 'plugin'],
"description": "Plugins to evaluate",
"required": True,
"help": "See activated plugins in /plugins",
"processor": API_PARAMS['algorithm']['processor']
},
"dataset": {
"aliases": ["datasets", "data", "d"],
"description": "Datasets to be evaluated",
"required": True,
"help": "See avalaible datasets in /datasets"
}
}
PLUGINS_PARAMS = {
"plugin-type": {
"@id": "pluginType",
"description": 'What kind of plugins to list',
"aliases": ["pluginType", "plugin_type"],
"required": True,
"default": 'analysisPlugin'
}
}
WEB_PARAMS = {
"in-headers": {
"aliases": ["headers", "inheaders", "inHeaders", "in-headers", "in_headers"],
"description": "Only include the JSON-LD context in the headers",
"inHeaders": {
"aliases": ["headers"],
"required": True,
"default": False,
"options": boolean
"options": "boolean"
},
}
CLI_PARAMS = {
"plugin-folder": {
"aliases": ["folder", "plugin_folder"],
"plugin_folder": {
"aliases": ["folder"],
"required": True,
"default": "."
},
@ -217,7 +85,6 @@ NIF_PARAMS = {
},
"intype": {
"@id": "intype",
"description": "input type",
"aliases": ["t"],
"required": False,
"default": "direct",
@ -225,44 +92,31 @@ NIF_PARAMS = {
},
"informat": {
"@id": "informat",
"description": "input format",
"aliases": ["f"],
"required": False,
"default": "text",
"options": ["text", "json-ld"],
"options": ["turtle", "text", "json-ld"],
},
"language": {
"@id": "language",
"description": "language of the input",
"aliases": ["l"],
"required": False,
},
"prefix": {
"@id": "prefix",
"description": "prefix to use for new entities",
"aliases": ["p"],
"required": True,
"default": "",
},
"urischeme": {
"@id": "urischeme",
"description": "scheme for NIF URIs",
"aliases": ["u"],
"required": False,
"default": "RFC5147String",
"options": ["RFC5147String", ]
"options": "RFC5147String"
}
}
BUILTIN_PARAMS = {}
for d in [
NIF_PARAMS, CLI_PARAMS, WEB_PARAMS, PLUGINS_PARAMS, EVAL_PARAMS,
API_PARAMS
]:
for k, v in d.items():
BUILTIN_PARAMS[k] = v
def parse_params(indict, *specs):
if not specs:
@ -272,154 +126,57 @@ def parse_params(indict, *specs):
wrong_params = {}
for spec in specs:
for param, options in iteritems(spec):
if param[0] != "@": # Exclude json-ld properties
for alias in options.get("aliases", []):
# Replace each alias with the correct name of the parameter
if alias in indict and alias != param:
if alias in indict and alias is not param:
outdict[param] = indict[alias]
del outdict[alias]
break
if param not in outdict:
if "default" in options:
# We assume the default is correct
outdict[param] = options["default"]
elif options.get("required", False):
wrong_params[param] = spec[param]
del indict[alias]
continue
if 'processor' in options:
outdict[param] = processors[options['processor']](outdict[param])
if "options" in options:
if options["options"] == boolean:
outdict[param] = str(outdict[param]).lower() in ['true', '1', '']
elif outdict[param] not in options["options"]:
if param not in outdict:
if options.get("required", False) and "default" not in options:
wrong_params[param] = spec[param]
else:
if "default" in options:
outdict[param] = options["default"]
elif "options" in spec[param]:
if spec[param]["options"] == "boolean":
outdict[param] = outdict[param] in [None, True, 'true', '1']
elif outdict[param] not in spec[param]["options"]:
wrong_params[param] = spec[param]
if wrong_params:
logger.debug("Error parsing: %s", wrong_params)
message = Error(
status=400,
message='Missing or invalid parameters',
message="Missing or invalid parameters",
parameters=outdict,
errors=wrong_params)
errors={param: error
for param, error in iteritems(wrong_params)})
raise message
if 'algorithm' in outdict and not isinstance(outdict['algorithm'], list):
outdict['algorithm'] = outdict['algorithm'].split(',')
return outdict
def get_all_params(plugins, *specs):
'''Return a list of parameters for a given set of specifications and plugins.'''
dic = {}
for s in specs:
dic.update(s)
dic.update(get_extra_params(plugins))
return dic
def get_extra_params(plugins):
'''Get a list of possible parameters given a list of plugins'''
params = {}
extra_params = {}
for plugin in plugins:
this_params = plugin.get('extra_params', {})
for k, v in this_params.items():
if k not in extra_params:
extra_params[k] = {}
extra_params[k][plugin.name] = v
for k, v in extra_params.items(): # Resolve conflicts
if len(v) == 1: # Add the extra options that do not collide
params[k] = list(v.values())[0]
else:
required = False
aliases = None
options = None
default = None
nodefault = False # Set when defaults are not compatible
for plugin, opt in v.items():
params['{}.{}'.format(plugin, k)] = opt
required = required or opt.get('required', False)
newaliases = set(opt.get('aliases', []))
if aliases is None:
aliases = newaliases
else:
aliases = aliases & newaliases
if 'options' in opt:
newoptions = set(opt['options'])
options = newoptions if options is None else options & newoptions
if 'default' in opt:
newdefault = opt['default']
if newdefault:
if default is None and not nodefault:
default = newdefault
elif newdefault != default:
nodefault = True
default = None
# Check for incompatibilities
if options != set():
params[k] = {
'default': default,
'aliases': list(aliases),
'required': required,
'options': list(options)
}
def get_extra_params(request, plugin=None):
params = request.parameters.copy()
if plugin:
extra_params = parse_params(params, plugin.get('extra_params', {}))
params.update(extra_params)
return params
def parse_analyses(params, plugins):
'''
Parse the given parameters individually for each plugin, and get a list of the parameters that
belong to each of the plugins. Each item can then be used in the plugin.analyse_entries method.
'''
analysis_list = []
for i, plugin in enumerate(plugins):
if not plugin:
continue
this_params = filter_params(params, plugin, i)
parsed = parse_params(this_params, plugin.get('extra_params', {}))
analysis = plugin.activity(parsed)
analysis_list.append(analysis)
return analysis_list
def filter_params(params, plugin, ith=-1):
'''
Get the values within params that apply to a plugin.
More specific names override more general names, in this order:
<index_order>.parameter > <plugin.name>.parameter > parameter
Example:
>>> filter_params({'0.hello': True, 'hello': False}, Plugin(), 0)
{ '0.hello': True, 'hello': True}
'''
thisparams = {}
if ith >= 0:
ith = '{}.'.format(ith)
else:
ith = ""
for k, v in params.items():
if ith and k.startswith(str(ith)):
thisparams[k[len(ith):]] = v
elif k.startswith(plugin.name):
thisparams[k[len(plugin.name) + 1:]] = v
elif k not in thisparams:
thisparams[k] = v
return thisparams
def parse_call(params):
'''
Return a results object based on the parameters used in a call/request.
'''Return a results object based on the parameters used in a call/request.
'''
params = parse_params(params, NIF_PARAMS)
if params['informat'] == 'text':
results = Results()
entry = Entry(nif__isString=params['input'], id='prefix:') # Use @base
entry = Entry(nif__isString=params['input'])
results.entries.append(entry)
elif params['informat'] == 'json-ld':
results = from_string(params['input'], cls=Results)
else: # pragma: no cover
raise NotImplementedError('Informat {} is not implemented'.format(
params['informat']))
else:
raise NotImplemented('Informat {} is not implemented'.format(params['informat']))
results.parameters = params
return results

View File

@ -1,7 +1,7 @@
#!/usr/bin/python
# -*- coding: utf-8 -*-
#
# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
# Copyright 2014 J. Fernando Sánchez Rada - Grupo de Sistemas Inteligentes
# DIT, UPM
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -14,47 +14,25 @@
# 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.
#
"""
Blueprints for Senpy
"""
from flask import (Blueprint, request, current_app, render_template, url_for,
jsonify, redirect)
from .models import Error, Response, Help, Plugins, read_schema, dump_schema, Datasets
jsonify)
from .models import Error, Response, Help, Plugins, read_schema
from . import api
from .version import __version__
from functools import wraps
from .gsitk_compat import GSITK_AVAILABLE, datasets
import logging
import json
import base64
logger = logging.getLogger(__name__)
api_blueprint = Blueprint("api", __name__)
demo_blueprint = Blueprint("demo", __name__, template_folder='templates')
demo_blueprint = Blueprint("demo", __name__)
ns_blueprint = Blueprint("ns", __name__)
_mimetypes_r = {'json-ld': ['application/ld+json'],
'turtle': ['text/turtle'],
'ntriples': ['application/n-triples'],
'text': ['text/plain']}
MIMETYPES = {}
for k, vs in _mimetypes_r.items():
for v in vs:
if v in MIMETYPES:
raise Exception('MIMETYPE {} specified for two formats: {} and {}'.format(v,
v,
MIMETYPES[v]))
MIMETYPES[v] = k
DEFAULT_MIMETYPE = 'application/ld+json'
DEFAULT_FORMAT = 'json-ld'
def get_params(req):
if req.method == 'POST':
@ -66,213 +44,106 @@ def get_params(req):
return indict
def encode_url(url=None):
code = ''
if not url:
url = request.parameters.get('prefix', request.full_path[1:] + '#')
return code or base64.urlsafe_b64encode(url.encode()).decode()
def url_for_code(code, base=None):
# if base:
# return base + code
# return url_for('api.decode', code=code, _external=True)
# This was producing unique yet very long URIs, which wasn't ideal for visualization.
return 'http://senpy.invalid/'
def decoded_url(code, base=None):
path = base64.urlsafe_b64decode(code.encode()).decode()
if path[:4] == 'http':
return path
base = base or request.url_root
return base + path
@demo_blueprint.route('/')
def index():
# ev = str(get_params(request).get('evaluation', True))
# evaluation_enabled = ev.lower() not in ['false', 'no', 'none']
evaluation_enabled = GSITK_AVAILABLE
return render_template("index.html",
evaluation=evaluation_enabled,
version=__version__)
return render_template("index.html", version=__version__)
@api_blueprint.route('/contexts/<code>')
def context(code=''):
@api_blueprint.route('/contexts/<entity>.jsonld')
def context(entity="context"):
context = Response._context
context['@base'] = url_for('api.decode', code=code, _external=True)
context['endpoint'] = url_for('api.api_root', _external=True)
context['@vocab'] = url_for('ns.index', _external=True)
return jsonify({"@context": context})
@api_blueprint.route('/d/<code>')
def decode(code):
try:
return redirect(decoded_url(code))
except Exception:
return Error('invalid URL').flask()
@ns_blueprint.route('/') # noqa: F811
def index():
context = Response._context.copy()
context['endpoint'] = url_for('api.api_root', _external=True)
context = Response._context
context['@vocab'] = url_for('.ns', _external=True)
return jsonify({"@context": context})
@api_blueprint.route('/schemas/<schema>')
def schema(schema="definitions"):
try:
return dump_schema(read_schema(schema))
except Exception as ex: # Should be FileNotFoundError, but it's missing from py2
return Error(message="Schema not found: {}".format(ex), status=404).flask()
return jsonify(read_schema(schema))
except Exception: # Should be FileNotFoundError, but it's missing from py2
return Error(message="Schema not found", status=404).flask()
def basic_api(f):
default_params = {
'in-headers': False,
'expanded-jsonld': False,
'outformat': None,
'with-parameters': True,
}
@wraps(f)
def decorated_function(*args, **kwargs):
raw_params = get_params(request)
# logger.info('Getting request: {}'.format(raw_params))
logger.debug('Getting request. Params: {}'.format(raw_params))
headers = {'X-ORIGINAL-PARAMS': json.dumps(raw_params)}
params = default_params
mime = request.accept_mimetypes\
.best_match(MIMETYPES.keys(),
DEFAULT_MIMETYPE)
mimeformat = MIMETYPES.get(mime, DEFAULT_FORMAT)
outformat = mimeformat
outformat = 'json-ld'
try:
print('Getting request:')
print(request)
params = api.parse_params(raw_params, api.WEB_PARAMS, api.API_PARAMS)
outformat = params.get('outformat', mimeformat)
if hasattr(request, 'parameters'):
request.parameters.update(params)
else:
request.parameters = params
response = f(*args, **kwargs)
except Error as ex:
response = ex
response.parameters = params
logger.error(ex)
if current_app.debug:
raise
if 'parameters' in response and not params['with-parameters']:
del response.parameters
logger.debug('Response: {}'.format(response))
prefix = params.get('prefix')
code = encode_url(prefix)
in_headers = params['inHeaders']
expanded = params['expanded-jsonld']
outformat = params['outformat']
return response.flask(
in_headers=params['in-headers'],
in_headers=in_headers,
headers=headers,
prefix=prefix or url_for_code(code),
base=prefix,
prefix=url_for('.api_root', _external=True),
context_uri=url_for('api.context',
code=code,
entity=type(response).__name__,
_external=True),
outformat=outformat,
expanded=params['expanded-jsonld'],
template=params.get('template'),
verbose=params['verbose'],
aliases=params['aliases'],
fields=params.get('fields'))
except (Exception) as ex:
if current_app.debug or current_app.config['TESTING']:
raise
if not isinstance(ex, Error):
msg = "{}".format(ex)
ex = Error(message=msg, status=500)
response = ex
response.parameters = raw_params
logger.exception(ex)
return response.flask(
outformat=outformat,
expanded=params['expanded-jsonld'],
verbose=params.get('verbose', True),
)
expanded=expanded)
return decorated_function
@api_blueprint.route('/', defaults={'plugins': None}, methods=['POST', 'GET'], strict_slashes=False)
@api_blueprint.route('/<path:plugins>', methods=['POST', 'GET'], strict_slashes=False)
@api_blueprint.route('/', methods=['POST', 'GET'])
@basic_api
def api_root(plugins):
if plugins:
if request.parameters['algorithm'] != api.API_PARAMS['algorithm']['default']:
raise Error('You cannot specify the algorithm with a parameter and a URL variable.'
' Please, remove one of them')
plugins = plugins.replace('+', ',').replace('/', ',')
plugins = api.processors['string_to_tuple'](plugins)
else:
plugins = request.parameters['algorithm']
print(plugins)
sp = current_app.senpy
plugins = sp.get_plugins(plugins)
def api_root():
if request.parameters['help']:
apis = [api.WEB_PARAMS, api.API_PARAMS, api.NIF_PARAMS]
# Verbose is set to False as default, but we want it to default to
# True for help. This checks the original value, to make sure it wasn't
# set by default.
if not request.parameters['verbose'] and get_params(request).get('verbose'):
apis = []
if request.parameters['algorithm'] == ['default', ]:
plugins = []
allparameters = api.get_all_params(plugins, *apis)
response = Help(valid_parameters=allparameters)
return response
req = api.parse_call(request.parameters)
analyses = api.parse_analyses(req.parameters, plugins)
results = current_app.senpy.analyse(req, analyses)
return results
@api_blueprint.route('/evaluate', methods=['POST', 'GET'], strict_slashes=False)
@basic_api
def evaluate():
if request.parameters['help']:
dic = dict(api.EVAL_PARAMS)
dic = dict(api.API_PARAMS, **api.NIF_PARAMS)
response = Help(parameters=dic)
return response
else:
params = api.parse_params(request.parameters, api.EVAL_PARAMS)
response = current_app.senpy.evaluate(params)
req = api.parse_call(request.parameters)
response = current_app.senpy.analyse(req)
return response
@api_blueprint.route('/plugins', methods=['POST', 'GET'], strict_slashes=False)
@api_blueprint.route('/plugins/', methods=['POST', 'GET'])
@basic_api
def plugins():
sp = current_app.senpy
params = api.parse_params(request.parameters, api.PLUGINS_PARAMS)
ptype = params.get('plugin-type')
plugins = list(sp.analysis_plugins(plugin_type=ptype))
dic = Plugins(plugins=plugins)
ptype = request.parameters.get('plugin_type')
plugins = sp.filter_plugins(plugin_type=ptype)
dic = Plugins(plugins=list(plugins.values()))
return dic
@api_blueprint.route('/plugins/<plugin>', methods=['POST', 'GET'], strict_slashes=False)
@api_blueprint.route('/plugins/<plugin>/', methods=['POST', 'GET'])
@basic_api
def plugin(plugin):
def plugin(plugin=None):
sp = current_app.senpy
return sp.get_plugin(plugin)
@api_blueprint.route('/datasets', methods=['POST', 'GET'], strict_slashes=False)
@basic_api
def get_datasets():
dic = Datasets(datasets=list(datasets.values()))
return dic
if plugin == 'default' and sp.default_plugin:
return sp.default_plugin
plugins = sp.filter_plugins(
id='plugins/{}'.format(plugin)) or sp.filter_plugins(name=plugin)
if plugins:
response = list(plugins.values())[0]
else:
return Error(message="Plugin not found", status=404)
return response

View File

@ -1,20 +1,3 @@
#
# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://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.
#
from __future__ import print_function
import sys
from .models import Error
from .extensions import Senpy
@ -44,16 +27,12 @@ def main_function(argv):
api.CLI_PARAMS,
api.API_PARAMS,
api.NIF_PARAMS)
plugin_folder = params['plugin-folder']
default_plugins = not params.get('no-default-plugins', False)
sp = Senpy(default_plugins=default_plugins, plugin_folder=plugin_folder)
plugin_folder = params['plugin_folder']
sp = Senpy(default_plugins=False, plugin_folder=plugin_folder)
request = api.parse_call(params)
algos = sp.get_plugins(request.parameters.get('algorithm', None))
if algos:
algos = request.parameters.get('algorithm', sp.plugins.keys())
for algo in algos:
sp.activate_plugin(algo.name)
else:
sp.activate_all()
sp.activate_plugin(algo)
res = sp.analyse(request)
return res
@ -63,9 +42,9 @@ def main():
'''
try:
res = main_function(sys.argv[1:])
print(res.serialize())
print(res.to_JSON())
except Error as err:
print(err.serialize(), file=sys.stderr)
print(err.to_JSON())
sys.exit(2)

View File

@ -1,22 +1,7 @@
#
# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://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.
#
import requests
import logging
from . import models
from .plugins import default_plugin_type
logger = logging.getLogger(__name__)
@ -28,24 +13,13 @@ class Client(object):
def analyse(self, input, method='GET', **kwargs):
return self.request('/', method=method, input=input, **kwargs)
def evaluate(self, input, method='GET', **kwargs):
return self.request('/evaluate', method=method, input=input, **kwargs)
def plugins(self, *args, **kwargs):
resp = self.request(path='/plugins').plugins
def plugins(self, ptype=default_plugin_type):
resp = self.request(path='/plugins', plugin_type=ptype).plugins
return {p.name: p for p in resp}
def datasets(self):
resp = self.request(path='/datasets').datasets
return {d.name: d for d in resp}
def request(self, path=None, method='GET', **params):
url = '{}{}'.format(self.endpoint.rstrip('/'), path)
if method == 'POST':
response = requests.post(url=url, data=params)
else:
url = '{}{}'.format(self.endpoint, path)
response = requests.request(method=method, url=url, params=params)
try:
resp = models.from_dict(response.json())
except Exception as ex:

View File

@ -1,7 +0,0 @@
import os
strict = os.environ.get('SENPY_STRICT', '').lower() not in ["", "false", "f"]
data_folder = os.environ.get('SENPY_DATA', None)
if data_folder:
data_folder = os.path.abspath(data_folder)
testing = os.environ.get('SENPY_TESTING', "") != ""

View File

@ -1,18 +1,3 @@
#
# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://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.
#
"""
Main class for Senpy.
It orchestrates plugin (de)activation and analysis.
@ -20,20 +5,19 @@ It orchestrates plugin (de)activation and analysis.
from future import standard_library
standard_library.install_aliases()
from . import config
from . import plugins, api
from .models import Error, AggregatedEvaluation
from .plugins import AnalysisPlugin
from .plugins import SenpyPlugin
from .models import Error
from .blueprints import api_blueprint, demo_blueprint, ns_blueprint
from threading import Thread
from functools import partial
import os
import copy
import errno
import logging
from . import gsitk_compat
import traceback
logger = logging.getLogger(__name__)
@ -45,38 +29,34 @@ class Senpy(object):
app=None,
plugin_folder=".",
data_folder=None,
install=False,
strict=None,
default_plugins=False):
default_data = os.path.join(os.getcwd(), 'senpy_data')
self.data_folder = data_folder or os.environ.get('SENPY_DATA', default_data)
try:
os.makedirs(self.data_folder)
except OSError as e:
if e.errno == errno.EEXIST:
logger.debug('Data folder exists: {}'.format(self.data_folder))
else: # pragma: no cover
raise
self.app = app
self._search_folders = set()
self._plugin_list = []
self._outdated = True
self._default = None
self.strict = strict if strict is not None else config.strict
self.install = install
self._plugins = {}
if plugin_folder:
self.add_folder(plugin_folder)
self.add_folder(plugin_folder)
if default_plugins:
self.add_folder('plugins', from_root=True)
else:
# Add only conversion plugins
self.add_folder(os.path.join('plugins', 'postprocessing'),
self.add_folder(os.path.join('plugins', 'conversion'),
from_root=True)
self.app = app
self.data_folder = data_folder or os.environ.get('SENPY_DATA',
os.path.join(os.getcwd(),
'senpy_data'))
try:
os.makedirs(self.data_folder)
except OSError as e:
if e.errno == errno.EEXIST:
print('Directory not created.')
else:
raise
if app is not None:
self.init_app(app)
self._conversion_candidates = {}
def init_app(self, app):
""" Initialise a flask app to add plugins to its context """
@ -89,121 +69,111 @@ class Senpy(object):
# otherwise fall back to the request context
if hasattr(app, 'teardown_appcontext'):
app.teardown_appcontext(self.teardown)
else: # pragma: no cover
else:
app.teardown_request(self.teardown)
app.register_blueprint(api_blueprint, url_prefix="/api")
app.register_blueprint(ns_blueprint, url_prefix="/ns")
app.register_blueprint(demo_blueprint, url_prefix="/")
def add_plugin(self, plugin):
self._plugins[plugin.name.lower()] = plugin
self._conversion_candidates = {}
def delete_plugin(self, plugin):
del self._plugins[plugin.name.lower()]
def plugins(self, plugin_type=None, is_activated=True, **kwargs):
""" Return the plugins registered for a given application. Filtered by criteria """
return sorted(plugins.pfilter(self._plugins,
plugin_type=plugin_type,
is_activated=is_activated,
**kwargs),
key=lambda x: x.id)
def get_plugin(self, name, default=None):
if name == 'default':
return self.default_plugin
elif name == 'conversion':
return None
if name.lower() in self._plugins:
return self._plugins[name.lower()]
results = self.plugins(id='endpoint:plugins/{}'.format(name.lower()),
plugin_type=None)
if results:
return results[0]
results = self.plugins(id=name,
plugin_type=None)
if results:
return results[0]
msg = ("Plugin not found: '{}'\n"
"Make sure it is ACTIVATED\n"
"Valid algorithms: {}").format(name,
self._plugins.keys())
raise Error(message=msg, status=404)
def get_plugins(self, name):
try:
name = name.split(',')
except AttributeError:
pass # Assume it is a tuple or a list
return tuple(self.get_plugin(n) for n in name)
def analysis_plugins(self, **kwargs):
""" Return only the analysis plugins that are active"""
candidates = self.plugins(**kwargs)
return list(plugins.pfilter(candidates, plugin_type=AnalysisPlugin))
def add_folder(self, folder, from_root=False):
""" Find plugins in this folder and add them to this instance """
if from_root:
folder = os.path.join(os.path.dirname(__file__), folder)
logger.debug("Adding folder: %s", folder)
if os.path.isdir(folder):
new_plugins = plugins.from_folder([folder],
data_folder=self.data_folder,
strict=self.strict)
for plugin in new_plugins:
self.add_plugin(plugin)
self._search_folders.add(folder)
self._outdated = True
else:
raise AttributeError("Not a folder or does not exist: %s", folder)
logger.debug("Not a folder: %s", folder)
def _process(self, req, pending, done=None):
def _get_plugins(self, request):
if not self.analysis_plugins:
raise Error(
status=404,
message=("No plugins found."
" Please install one."))
algos = request.parameters.get('algorithm', None)
if not algos:
if self.default_plugin:
algos = [self.default_plugin.name, ]
else:
raise Error(
status=404,
message="No default plugin found, and None provided")
plugins = list()
for algo in algos:
if algo not in self.plugins:
logger.debug(("The algorithm '{}' is not valid\n"
"Valid algorithms: {}").format(algo,
self.plugins.keys()))
raise Error(
status=404,
message="The algorithm '{}' is not valid".format(algo))
plugins.append(self.plugins[algo])
return plugins
def _process_entries(self, entries, req, plugins):
"""
Recursively process the entries with the first plugin in the list, and pass the results
to the rest of the plugins.
"""
done = done or []
if not pending:
return req
analysis = pending[0]
results = analysis.run(req)
results.activities.append(analysis)
done += analysis
return self._process(results, pending[1:], done)
if not plugins:
for i in entries:
yield i
return
plugin = plugins[0]
self._activate(plugin) # Make sure the plugin is activated
specific_params = api.get_extra_params(req, plugin)
req.analysis.append({'plugin': plugin,
'parameters': specific_params})
results = plugin.analyse_entries(entries, specific_params)
for i in self._process_entries(results, req, plugins[1:]):
yield i
def install_deps(self):
logger.info('Installing dependencies')
# If a plugin is activated, its dependencies should already be installed
# Otherwise, it would've failed to activate.
plugins.install_deps(*self._plugins.values())
for plugin in self.filter_plugins(is_activated=True):
plugins.install_deps(plugin)
def analyse(self, request, analyses=None):
def analyse(self, request):
"""
Main method that analyses a request, either from CLI or HTTP.
It takes a processed request, provided by the user, as returned
by api.parse_call().
"""
if not self.plugins():
raise Error(
status=404,
message=("No plugins found."
" Please install one."))
if analyses is None:
plugins = self.get_plugins(request.parameters['algorithm'])
analyses = api.parse_analyses(request.parameters, plugins)
logger.debug("analysing request: {}".format(request))
results = self._process(request, analyses)
logger.debug("Got analysis result: {}".format(results))
results = self.postprocess(results, analyses)
logger.debug("Returning post-processed result: {}".format(results))
try:
entries = request.entries
request.entries = []
plugins = self._get_plugins(request)
results = request
for i in self._process_entries(entries, results, plugins):
results.entries.append(i)
self.convert_emotions(results)
if 'with_parameters' not in results.parameters:
del results.parameters
logger.debug("Returning analysis result: {}".format(results))
except (Error, Exception) as ex:
if not isinstance(ex, Error):
msg = "Error during analysis: {} \n\t{}".format(ex,
traceback.format_exc())
ex = Error(message=msg, status=500)
logger.exception('Error returning analysis result')
raise ex
results.analysis = [i['plugin'].id for i in results.analysis]
return results
def convert_emotions(self, resp, analyses):
def _conversion_candidates(self, fromModel, toModel):
candidates = self.filter_plugins(plugin_type='emotionConversionPlugin')
for name, candidate in candidates.items():
for pair in candidate.onyx__doesConversion:
logging.debug(pair)
if pair['onyx:conversionFrom'] == fromModel \
and pair['onyx:conversionTo'] == toModel:
# logging.debug('Found candidate: {}'.format(candidate))
yield candidate
def convert_emotions(self, resp):
"""
Conversion of all emotions in a response **in place**.
In addition to converting from one model to another, it has
@ -211,187 +181,110 @@ class Senpy(object):
Needless to say, this is far from an elegant solution, but it works.
@todo refactor and clean up
"""
logger.debug("Converting emotions")
if 'parameters' not in resp:
logger.debug("NO PARAMETERS")
return resp
params = resp['parameters']
toModel = params.get('emotion-model', None)
plugins = [i['plugin'] for i in resp.analysis]
params = resp.parameters
toModel = params.get('emotionModel', None)
if not toModel:
logger.debug("NO tomodel PARAMETER")
return resp
return
logger.debug('Asked for model: {}'.format(toModel))
output = params.get('conversion', None)
candidates = {}
for plugin in plugins:
try:
fromModel = plugin.get('onyx:usesEmotionModel', None)
candidates[plugin.id] = next(self._conversion_candidates(fromModel, toModel))
logger.debug('Analysis plugin {} uses model: {}'.format(plugin.id, fromModel))
except StopIteration:
e = Error(('No conversion plugin found for: '
'{} -> {}'.format(fromModel, toModel)))
e.original_response = resp
e.parameters = params
raise e
newentries = []
done = []
for i in resp.entries:
if output == "full":
newemotions = copy.deepcopy(i.emotions)
else:
newemotions = []
for j in i.emotions:
activity = j['prov:wasGeneratedBy']
act = resp.activity(activity)
if not act:
raise Error('Could not find the emotion model for {}'.format(activity))
fromModel = act.plugin['onyx:usesEmotionModel']
if toModel == fromModel:
continue
candidate = self._conversion_candidate(fromModel, toModel)
if not candidate:
e = Error(('No conversion plugin found for: '
'{} -> {}'.format(fromModel, toModel)),
status=404)
e.original_response = resp
e.parameters = params
raise e
analysis = candidate.activity(params)
done.append(analysis)
plugname = j['prov:wasGeneratedBy']
candidate = candidates[plugname]
resp.analysis.append({'plugin': candidate,
'parameters': params})
for k in candidate.convert(j, fromModel, toModel, params):
k.prov__wasGeneratedBy = analysis.id
k.prov__wasGeneratedBy = candidate.id
if output == 'nested':
k.prov__wasDerivedFrom = j
newemotions.append(k)
i.emotions = newemotions
newentries.append(i)
resp.entries = newentries
return resp
def _conversion_candidate(self, fromModel, toModel):
if not self._conversion_candidates:
candidates = {}
for conv in self.plugins(plugin_type=plugins.EmotionConversion):
for pair in conv.onyx__doesConversion:
logging.debug(pair)
key = (pair['onyx:conversionFrom'], pair['onyx:conversionTo'])
if key not in candidates:
candidates[key] = []
candidates[key].append(conv)
self._conversion_candidates = candidates
key = (fromModel, toModel)
if key not in self._conversion_candidates:
return None
return self._conversion_candidates[key][0]
def postprocess(self, response, analyses):
'''
Transform the results from the analysis plugins.
It has some pre-defined post-processing like emotion conversion,
and it also allows plugins to auto-select themselves.
'''
response = self.convert_emotions(response, analyses)
for plug in self.plugins(plugin_type=plugins.PostProcessing):
if plug.check(response, response.activities):
activity = plug.activity(response.parameters)
response = plug.process(response, activity)
return response
def _get_datasets(self, request):
datasets_name = request.parameters.get('dataset', None).split(',')
for dataset in datasets_name:
if dataset not in gsitk_compat.datasets:
logger.debug(("The dataset '{}' is not valid\n"
"Valid datasets: {}").format(
dataset, gsitk_compat.datasets.keys()))
raise Error(
status=404,
message="The dataset '{}' is not valid".format(dataset))
return datasets_name
def evaluate(self, params):
logger.debug("evaluating request: {}".format(params))
results = AggregatedEvaluation()
results.parameters = params
datasets = self._get_datasets(results)
plugs = []
for plugname in params['algorithm']:
plugs = self.get_plugins(plugname)
for plug in plugs:
if not isinstance(plug, plugins.Evaluable):
raise Exception('Plugin {} can not be evaluated', plug.id)
for eval in plugins.evaluate(plugs, datasets):
results.evaluations.append(eval)
if 'with-parameters' not in results.parameters:
del results.parameters
logger.debug("Returning evaluation result: {}".format(results))
return results
@property
def default_plugin(self):
if not self._default or not self._default.is_activated:
candidates = self.analysis_plugins()
candidate = self._default
if not candidate:
candidates = self.filter_plugins(plugin_type='analysisPlugin',
is_activated=True)
if len(candidates) > 0:
self._default = candidates[0]
else:
self._default = None
logger.debug("Default: {}".format(self._default))
return self._default
candidate = list(candidates.values())[0]
logger.debug("Default: {}".format(candidate))
return candidate
@default_plugin.setter
def default_plugin(self, value):
if isinstance(value, plugins.Plugin):
if not value.is_activated:
raise AttributeError('The default plugin has to be activated.')
if isinstance(value, SenpyPlugin):
self._default = value
else:
self._default = self._plugins[value.lower()]
self._default = self.plugins[value]
def activate_all(self, sync=True):
ps = []
for plug in self._plugins.keys():
try:
self.activate_plugin(plug, sync=sync)
except Exception as ex:
if self.strict:
raise
logger.error('Could not activate {}: {}'.format(plug, ex))
for plug in self.plugins.keys():
ps.append(self.activate_plugin(plug, sync=sync))
return ps
def deactivate_all(self, sync=True):
ps = []
for plug in self._plugins.keys():
for plug in self.plugins.keys():
ps.append(self.deactivate_plugin(plug, sync=sync))
return ps
def _set_active(self, plugin, active=True, *args, **kwargs):
''' We're using a variable in the plugin itself to activate/deactive plugins.\
Note that plugins may activate themselves by setting this variable.
'''
plugin.is_activated = active
def _activate(self, plugin):
success = False
with plugin._lock:
if plugin.is_activated:
return
try:
logger.info("Activating plugin: {}".format(plugin.name))
assert plugin._activate()
logger.info(f"Plugin activated: {plugin.name}")
plugin.activate()
msg = "Plugin activated: {}".format(plugin.name)
logger.info(msg)
success = True
self._set_active(plugin, success)
except Exception as ex:
if getattr(plugin, "optional", False) and not self.strict:
logger.info(f"Plugin could NOT be activated: {plugin.name}")
return False
raise
return plugin.is_activated
msg = "Error activating plugin {} - {} : \n\t{}".format(
plugin.name, ex, traceback.format_exc())
logger.error(msg)
raise Error(msg)
def activate_plugin(self, plugin_name, sync=True):
plugin_name = plugin_name.lower()
if plugin_name not in self._plugins:
try:
plugin = self.plugins[plugin_name]
except KeyError:
raise Error(
message="Plugin not found: {}".format(plugin_name), status=404)
plugin = self._plugins[plugin_name]
logger.info("Activating plugin: {}".format(plugin.name))
if sync or not getattr(plugin, 'async', True) or getattr(
plugin, 'sync', False):
return self._activate(plugin)
if sync or 'async' in plugin and not plugin.async:
self._activate(plugin)
else:
th = Thread(target=partial(self._activate, plugin))
th.start()
@ -401,23 +294,46 @@ class Senpy(object):
with plugin._lock:
if not plugin.is_activated:
return
plugin._deactivate()
try:
plugin.deactivate()
logger.info("Plugin deactivated: {}".format(plugin.name))
except Exception as ex:
logger.error(
"Error deactivating plugin {}: {}".format(plugin.name, ex))
logger.error("Trace: {}".format(traceback.format_exc()))
def deactivate_plugin(self, plugin_name, sync=True):
plugin_name = plugin_name.lower()
if plugin_name not in self._plugins:
try:
plugin = self.plugins[plugin_name]
except KeyError:
raise Error(
message="Plugin not found: {}".format(plugin_name), status=404)
plugin = self._plugins[plugin_name]
if sync or not getattr(plugin, 'async', True) or not getattr(
plugin, 'sync', False):
plugin._deactivate()
self._set_active(plugin, False)
if sync or 'async' in plugin and not plugin.async:
self._deactivate(plugin)
else:
th = Thread(target=plugin.deactivate)
th = Thread(target=partial(self._deactivate, plugin))
th.start()
return th
def teardown(self, exception):
pass
@property
def plugins(self):
""" Return the plugins registered for a given application. """
if self._outdated:
self._plugin_list = plugins.load_plugins(self._search_folders,
data_folder=self.data_folder)
self._outdated = False
return self._plugin_list
def filter_plugins(self, **kwargs):
return plugins.pfilter(self.plugins, **kwargs)
@property
def analysis_plugins(self):
""" Return only the analysis plugins """
return self.filter_plugins(plugin_type='analysisPlugin')

View File

@ -1,67 +0,0 @@
#
# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://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.
#
import logging
import os
from pkg_resources import parse_version, get_distribution, DistributionNotFound
logger = logging.getLogger(__name__)
MSG = 'GSITK is not (properly) installed.'
IMPORTMSG = '{} Some functions will be unavailable.'.format(MSG)
RUNMSG = '{} Install it to use this function.'.format(MSG)
def raise_exception(*args, **kwargs):
raise Exception(RUNMSG)
try:
gsitk_distro = get_distribution("gsitk")
GSITK_VERSION = parse_version(gsitk_distro.version)
if not os.environ.get('DATA_PATH'):
os.environ['DATA_PATH'] = os.environ.get('SENPY_DATA', 'senpy_data')
from gsitk.datasets.datasets import DatasetManager
from gsitk.evaluation.evaluation import Evaluation as Eval # noqa: F401
from gsitk.evaluation.evaluation import EvalPipeline # noqa: F401
from sklearn.pipeline import Pipeline
modules = locals()
GSITK_AVAILABLE = True
datasets = {}
manager = DatasetManager()
for item in manager.get_datasets():
for key in item:
if key in datasets:
continue
properties = item[key]
properties['@id'] = key
datasets[key] = properties
def prepare(ds, *args, **kwargs):
return manager.prepare_datasets(ds, *args, **kwargs)
except (DistributionNotFound, ImportError) as err:
logger.debug('Error importing GSITK: {}'.format(err))
logger.warning(IMPORTMSG)
GSITK_AVAILABLE = False
GSITK_VERSION = ()
DatasetManager = Eval = Pipeline = prepare = raise_exception
datasets = {}

View File

@ -1,303 +0,0 @@
#
# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://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.
#
'''
Meta-programming for the models.
'''
import os
import json
import jsonschema
import inspect
import copy
from abc import ABCMeta
from collections import namedtuple
from collections.abc import MutableMapping
class BaseMeta(ABCMeta):
'''
Metaclass for models. It extracts the default values for the fields in
the model.
For instance, instances of the following class wouldn't need to mark
their version or description on initialization:
.. code-block:: python
class MyPlugin(Plugin):
version=0.3
description='A dull plugin'
Note that these operations could be included in the __init__ of the
class, but it would be very inefficient.
'''
_subtypes = {}
def __new__(mcs, name, bases, attrs, **kwargs):
register_afterwards = False
defaults = {}
aliases = {}
attrs = mcs.expand_with_schema(name, attrs)
if 'schema' in attrs:
register_afterwards = True
for base in bases:
if hasattr(base, '_defaults'):
defaults.update(getattr(base, '_defaults'))
if hasattr(base, '_aliases'):
aliases.update(getattr(base, '_aliases'))
info, rest = mcs.split_attrs(attrs)
for i in list(info.keys()):
if isinstance(info[i], _Alias):
aliases[i] = info[i].indict
if info[i].default is not None:
defaults[i] = info[i].default
else:
defaults[i] = info[i]
rest['_defaults'] = defaults
rest['_aliases'] = aliases
cls = super(BaseMeta, mcs).__new__(mcs, name, tuple(bases), rest)
if register_afterwards:
mcs.register(cls, defaults['@type'])
return cls
@classmethod
def register(mcs, rsubclass, rtype=None):
mcs._subtypes[rtype or rsubclass.__name__] = rsubclass
@staticmethod
def expand_with_schema(name, attrs):
if 'schema' in attrs: # Schema specified by name
schema_file = '{}.json'.format(attrs['schema'])
elif 'schema_file' in attrs:
schema_file = attrs['schema_file']
del attrs['schema_file']
else:
return attrs
if '/' not in 'schema_file':
thisdir = os.path.dirname(os.path.realpath(__file__))
schema_file = os.path.join(thisdir,
'schemas',
schema_file)
schema_path = 'file://' + schema_file
with open(schema_file) as f:
schema = json.load(f)
resolver = jsonschema.RefResolver(schema_path, schema)
if '@type' not in attrs:
attrs['@type'] = name
attrs['_schema_file'] = schema_file
attrs['schema'] = schema
attrs['_validator'] = jsonschema.Draft4Validator(schema, resolver=resolver)
schema_defaults = BaseMeta.get_defaults(attrs['schema'])
attrs.update(schema_defaults)
return attrs
@staticmethod
def is_func(v):
return inspect.isroutine(v) or inspect.ismethod(v) or \
inspect.ismodule(v) or isinstance(v, property)
@staticmethod
def is_internal(k):
return k[0] == '_' or k == 'schema' or k == 'data'
@staticmethod
def get_key(key):
if key[0] != '_':
key = key.replace("__", ":", 1)
return key
@staticmethod
def split_attrs(attrs):
'''
Extract the attributes of the class.
This allows adding default values in the class definition.
e.g.:
'''
isattr = {}
rest = {}
for key, value in attrs.items():
if not (BaseMeta.is_internal(key)) and (not BaseMeta.is_func(value)):
isattr[key] = value
else:
rest[key] = value
return isattr, rest
@staticmethod
def get_defaults(schema):
temp = {}
for obj in [
schema,
] + schema.get('allOf', []):
for k, v in obj.get('properties', {}).items():
if 'default' in v and k not in temp:
temp[k] = v['default']
return temp
def make_property(key, default=None):
def fget(self):
if default:
return self.get(key, copy.copy(default))
return self[key]
def fdel(self):
del self[key]
def fset(self, value):
self[key] = value
return fget, fset, fdel
class CustomDict(MutableMapping, object):
'''
A dictionary whose elements can also be accessed as attributes. Since some
characters are not valid in the dot-notation, the attribute names also
converted. e.g.:
> d = CustomDict()
> d.key = d['ns:name'] = 1
> d.key == d['key']
True
> d.ns__name == d['ns:name']
'''
_defaults = {}
_aliases = {'id': '@id'}
def __init__(self, *args, **kwargs):
super(CustomDict, self).__init__()
for k, v in self._defaults.items():
self[k] = copy.copy(v)
for arg in args:
self.update(arg)
for k, v in kwargs.items():
self[k] = v
return self
def serializable(self, **kwargs):
def ser_or_down(item):
if hasattr(item, 'serializable'):
return item.serializable(**kwargs)
elif isinstance(item, dict):
temp = dict()
for kp in item:
vp = item[kp]
temp[kp] = ser_or_down(vp)
return temp
elif isinstance(item, list) or isinstance(item, set):
return list(ser_or_down(i) for i in item)
else:
return item
return ser_or_down(self.as_dict(**kwargs))
def __getitem__(self, key):
return self.__dict__[key]
def __setitem__(self, key, value):
'''Do not insert data directly, there might be a property in that key. '''
key = self._key_to_attr(key)
return setattr(self, key, value)
def __delitem__(self, key):
key = self._key_to_attr(key)
del self.__dict__[key]
def as_dict(self, verbose=True, aliases=False):
attrs = self.__dict__.keys()
if not verbose and hasattr(self, '_terse_keys'):
attrs = self._terse_keys + ['@type', '@id']
res = {k: getattr(self, k) for k in attrs
if not self._internal_key(k) and hasattr(self, k)}
if not aliases:
return res
for k, ok in self._aliases.items():
if ok in res:
res[k] = getattr(res, ok)
del res[ok]
return res
def __iter__(self):
return (k for k in self.__dict__ if not self._internal_key(k))
def __len__(self):
return len(self.__dict__)
def update(self, other):
for k, v in other.items():
self[k] = v
def _attr_to_key(self, key):
key = key.replace("__", ":", 1)
key = self._aliases.get(key, key)
return key
def _key_to_attr(self, key):
if self._internal_key(key):
return key
if key in self._aliases:
key = self._aliases[key]
else:
key = key.replace(":", "__", 1)
return key
def __getattr__(self, key):
nkey = self._attr_to_key(key)
if nkey in self.__dict__:
return self.__dict__[nkey]
elif nkey == key:
raise AttributeError("Key not found: {}".format(key))
return getattr(self, nkey)
def __setattr__(self, key, value):
super(CustomDict, self).__setattr__(self._attr_to_key(key), value)
def __delattr__(self, key):
super(CustomDict, self).__delattr__(self._attr_to_key(key))
@staticmethod
def _internal_key(key):
return key[0] == '_'
def __str__(self):
return json.dumps(self.serializable(), sort_keys=True, indent=4)
def __repr__(self):
return json.dumps(self.serializable(), sort_keys=True, indent=4)
_Alias = namedtuple('Alias', ['indict', 'default'])
def alias(key, default=None):
return _Alias(key, default)

View File

@ -1,18 +1,3 @@
#
# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://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.
#
'''
Senpy Models.
@ -21,37 +6,27 @@ For compatibility with Py3 and for easier debugging, this new version drops
introspection and adds all arguments to the models.
'''
from __future__ import print_function
from future import standard_library
standard_library.install_aliases()
from future.utils import with_metaclass
from past.builtins import basestring
from jinja2 import Environment, BaseLoader
from six import string_types
import time
import copy
import json
import os
import jsonref
import jsonschema
from flask import Response as FlaskResponse
from pyld import jsonld
import logging
import jmespath
logging.getLogger('rdflib').setLevel(logging.WARN)
logger = logging.getLogger(__name__)
from rdflib import Graph
import logging
from .meta import BaseMeta, CustomDict, alias
logger = logging.getLogger(__name__)
DEFINITIONS_FILE = 'definitions.json'
CONTEXT_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)),
'schemas',
'context.jsonld')
CONTEXT_PATH = os.path.join(
os.path.dirname(os.path.realpath(__file__)), 'schemas', 'context.jsonld')
def get_schema_path(schema_file, absolute=False):
@ -70,99 +45,40 @@ def read_schema(schema_file, absolute=False):
return jsonref.load(f, base_uri=schema_uri)
def dump_schema(schema):
return jsonref.dumps(schema)
base_schema = read_schema(DEFINITIONS_FILE)
def load_context(context):
class Context(dict):
@staticmethod
def load(context):
logging.debug('Loading context: {}'.format(context))
if not context:
return context
elif isinstance(context, list):
contexts = []
for c in context:
contexts.append(load_context(c))
contexts.append(Context.load(c))
return contexts
elif isinstance(context, dict):
return dict(context)
elif isinstance(context, basestring):
return Context(context)
elif isinstance(context, string_types):
try:
with open(context) as f:
return dict(json.loads(f.read()))
return Context(json.loads(f.read()))
except IOError:
return context
else:
raise AttributeError('Please, provide a valid context')
base_context = load_context(CONTEXT_PATH)
base_context = Context.load(CONTEXT_PATH)
def register(rsubclass, rtype=None):
BaseMeta.register(rsubclass, rtype)
class BaseModel(with_metaclass(BaseMeta, CustomDict)):
'''
Entities of the base model are a special kind of dictionary that emulates
a JSON-LD object. The structure of the dictionary is checked via JSON-schema.
For convenience, the values can also be accessed as attributes
(a la Javascript). e.g.:
>>> myobject.key == myobject['key']
True
>>> myobject.ns__name == myobject['ns:name']
True
Additionally, subclasses of this class can specify default values for their
instances. These defaults are inherited by subclasses. e.g.:
>>> class NewModel(BaseModel):
... mydefault = 5
>>> n1 = NewModel()
>>> n1['mydefault'] == 5
True
>>> n1.mydefault = 3
>>> n1['mydefault'] = 3
True
>>> n2 = NewModel()
>>> n2 == 5
True
>>> class SubModel(NewModel):
pass
>>> subn = SubModel()
>>> subn.mydefault == 5
True
Lastly, every subclass that also specifies a schema will get registered, so it
is possible to deserialize JSON and get the right type.
i.e. to recover an instance of the original class from a plain JSON.
'''
# schema_file = DEFINITIONS_FILE
class SenpyMixin(object):
_context = base_context["@context"]
def __init__(self, *args, **kwargs):
auto_id = kwargs.pop('_auto_id', False)
super(BaseModel, self).__init__(*args, **kwargs)
if auto_id:
self.id
@property
def id(self):
if '@id' not in self:
self['@id'] = 'prefix:{}_{}'.format(type(self).__name__, time.time())
return self['@id']
@id.setter
def id(self, value):
self['@id'] = value
def flask(self,
in_headers=False,
in_headers=True,
headers=None,
outformat='json-ld',
**kwargs):
@ -186,37 +102,26 @@ class BaseModel(with_metaclass(BaseMeta, CustomDict)):
})
return FlaskResponse(
response=content,
status=self.get('status', 200),
status=getattr(self, "status", 200),
headers=headers,
mimetype=mimetype)
def serialize(self, format='json-ld', with_mime=False,
template=None, prefix=None, fields=None, **kwargs):
js = self.jsonld(prefix=prefix, **kwargs)
if template is not None:
rtemplate = Environment(loader=BaseLoader).from_string(template)
content = rtemplate.render(**self)
mimetype = 'text'
elif fields is not None:
# Emulate field selection by constructing a template
content = json.dumps(jmespath.search(fields, js))
mimetype = 'text'
elif format == 'json-ld':
def serialize(self, format='json-ld', with_mime=False, **kwargs):
js = self.jsonld(**kwargs)
if format == 'json-ld':
content = json.dumps(js, indent=2, sort_keys=True)
mimetype = "application/json"
elif format in ['turtle', 'ntriples']:
content = json.dumps(js, indent=2, sort_keys=True)
elif format in ['turtle', ]:
logger.debug(js)
context = [self._context, {'prefix': prefix, '@base': prefix}]
content = json.dumps(js, indent=2, sort_keys=True)
g = Graph().parse(
data=content,
format='json-ld',
prefix=prefix,
context=context)
base=kwargs.get('prefix'),
context=self._context)
logger.debug(
'Parsing with prefix: {}'.format(kwargs.get('prefix')))
content = g.serialize(format=format,
prefix=prefix)
content = g.serialize(format='turtle').decode('utf-8')
mimetype = 'text/{}'.format(format)
else:
raise Error('Unknown outformat: {}'.format(format))
@ -225,41 +130,52 @@ class BaseModel(with_metaclass(BaseMeta, CustomDict)):
else:
return content
def serializable(self):
def ser_or_down(item):
if hasattr(item, 'serializable'):
return item.serializable()
elif isinstance(item, dict):
temp = dict()
for kp in item:
vp = item[kp]
temp[kp] = ser_or_down(vp)
return temp
elif isinstance(item, list) or isinstance(item, set):
return list(ser_or_down(i) for i in item)
else:
return item
return ser_or_down(self._plain_dict())
def jsonld(self,
with_context=False,
with_context=True,
context_uri=None,
prefix=None,
base=None,
expanded=False,
**kwargs):
result = self.serializable(**kwargs)
expanded=False):
ser = self.serializable()
result = jsonld.compact(
ser,
self._context,
options={
'base': prefix,
'expandContext': self._context,
'senpy': prefix
})
if context_uri:
result['@context'] = context_uri
if expanded:
result = jsonld.expand(
result,
options={
'expandContext': [
self._context,
{
'prefix': prefix,
'endpoint': prefix
}
]
}
)[0]
result, options={'base': prefix,
'expandContext': self._context})
if not with_context:
try:
del result['@context']
except KeyError:
pass
elif context_uri:
result['@context'] = context_uri
else:
result['@context'] = self._context
return result
def to_JSON(self, *args, **kwargs):
js = json.dumps(self.jsonld(*args, **kwargs), indent=4, sort_keys=True)
return js
def validate(self, obj=None):
if not obj:
obj = self
@ -267,26 +183,86 @@ class BaseModel(with_metaclass(BaseMeta, CustomDict)):
obj = obj.jsonld()
self._validator.validate(obj)
def prov(self, another):
self['prov:wasGeneratedBy'] = another.id
def __str__(self):
return str(self.serialize())
def subtypes():
return BaseMeta._subtypes
class BaseModel(SenpyMixin, dict):
schema = base_schema
def __init__(self, *args, **kwargs):
if 'id' in kwargs:
self.id = kwargs.pop('id')
elif kwargs.pop('_auto_id', True):
self.id = '_:{}_{}'.format(type(self).__name__, time.time())
temp = dict(*args, **kwargs)
for obj in [
self.schema,
] + self.schema.get('allOf', []):
for k, v in obj.get('properties', {}).items():
if 'default' in v and k not in temp:
temp[k] = copy.deepcopy(v['default'])
for i in temp:
nk = self._get_key(i)
if nk != i:
temp[nk] = temp[i]
del temp[i]
try:
temp['@type'] = getattr(self, '@type')
except AttributeError:
logger.warn('Creating an instance of an unknown model')
super(BaseModel, self).__init__(temp)
def _get_key(self, key):
if key is 'id':
key = '@id'
key = key.replace("__", ":", 1)
return key
def __delitem__(self, key):
dict.__delitem__(self, key)
def __getattr__(self, key):
try:
return self.__getitem__(self._get_key(key))
except KeyError:
raise AttributeError(key)
def __setattr__(self, key, value):
self.__setitem__(self._get_key(key), value)
def __delattr__(self, key):
try:
object.__delattr__(self, key)
except AttributeError:
self.__delitem__(self._get_key(key))
def _plain_dict(self):
d = {k: v for (k, v) in self.items() if k[0] != "_"}
return d
def from_dict(indict, cls=None, warn=True):
def register(rsubclass, rtype=None):
_subtypes[rtype or rsubclass.__name__] = rsubclass
_subtypes = {}
def from_dict(indict, cls=None):
if not cls:
target = indict.get('@type', None)
cls = BaseModel
try:
cls = subtypes()[target]
except KeyError:
pass
if cls == BaseModel and warn:
logger.warning('Created an instance of an unknown model')
if target and target in _subtypes:
cls = _subtypes[target]
else:
cls = BaseModel
except Exception:
cls = BaseModel
outdict = dict()
for k, v in indict.items():
if k == '@context':
@ -294,11 +270,10 @@ def from_dict(indict, cls=None, warn=True):
elif isinstance(v, dict):
v = from_dict(indict[k])
elif isinstance(v, list):
v = v[:]
for ix, v2 in enumerate(v):
if isinstance(v2, dict):
v[ix] = from_dict(v2)
outdict[k] = copy.copy(v)
outdict[k] = v
return cls(**outdict)
@ -306,226 +281,104 @@ def from_string(string, **kwargs):
return from_dict(json.loads(string), **kwargs)
def from_json(injson, **kwargs):
def from_json(injson):
indict = json.loads(injson)
return from_dict(indict, **kwargs)
return from_dict(indict)
class Entry(BaseModel):
schema = 'entry'
text = alias('nif:isString')
sentiments = alias('marl:hasOpinion', [])
emotions = alias('onyx:hasEmotionSet', [])
class Sentiment(BaseModel):
schema = 'sentiment'
polarity = alias('marl:hasPolarity')
polarityValue = alias('marl:polarityValue')
class Error(BaseModel, Exception):
schema = 'error'
def __init__(self, message='Generic senpy exception', *args, **kwargs):
Exception.__init__(self, message)
super(Error, self).__init__(*args, **kwargs)
self.message = message
def __str__(self):
if not hasattr(self, 'errors'):
return self.message
return '{}:\n\t{}'.format(self.message, self.errors)
def __hash__(self):
return Exception.__hash__(self)
class AggregatedEvaluation(BaseModel):
schema = 'aggregatedEvaluation'
evaluations = alias('senpy:evaluations', [])
class Dataset(BaseModel):
schema = 'dataset'
class Datasets(BaseModel):
schema = 'datasets'
datasets = []
class Emotion(BaseModel):
schema = 'emotion'
class EmotionConversion(BaseModel):
schema = 'emotionConversion'
class EmotionConversionPlugin(BaseModel):
schema = 'emotionConversionPlugin'
class EmotionAnalysis(BaseModel):
schema = 'emotionAnalysis'
class EmotionModel(BaseModel):
schema = 'emotionModel'
onyx__hasEmotionCategory = []
class EmotionPlugin(BaseModel):
schema = 'emotionPlugin'
class EmotionSet(BaseModel):
schema = 'emotionSet'
onyx__hasEmotion = []
class Evaluation(BaseModel):
schema = 'evaluation'
metrics = alias('senpy:metrics', [])
class Entity(BaseModel):
schema = 'entity'
class Help(BaseModel):
schema = 'help'
class Metric(BaseModel):
schema = 'metric'
class Parameter(BaseModel):
schema = 'parameter'
class Plugins(BaseModel):
schema = 'plugins'
plugins = []
class Response(BaseModel):
schema = 'response'
class Results(BaseModel):
schema = 'results'
_terse_keys = ['entries', ]
activities = []
entries = []
def activity(self, id):
for i in self.activities:
if i.id == id:
return i
return None
class SentimentPlugin(BaseModel):
schema = 'sentimentPlugin'
class Suggestion(BaseModel):
schema = 'suggestion'
class Topic(BaseModel):
schema = 'topic'
class Analysis(BaseModel):
'''
A prov:Activity that results of executing a Plugin on an entry with a set of
parameters.
'''
schema = 'analysis'
parameters = alias('prov:used', [])
algorithm = alias('prov:wasAssociatedWith', [])
@property
def params(self):
outdict = {}
outdict['algorithm'] = self.algorithm
for param in self.parameters:
outdict[param['name']] = param['value']
return outdict
@params.setter
def params(self, value):
for k, v in value.items():
for param in self.parameters:
if param.name == k:
param.value = v
break
else:
self.parameters.append(Parameter(name=k, value=v)) # noqa: F821
def param(self, key, default=None):
for param in self.parameters:
if param['name'] == key:
return param['value']
return default
@property
def plugin(self):
return self._plugin
@plugin.setter
def plugin(self, value):
self._plugin = value
self['prov:wasAssociatedWith'] = value.id
def run(self, request):
return self.plugin.process(request, self)
class Plugin(BaseModel):
schema = 'plugin'
extra_params = {}
def activity(self, parameters=None):
'''Generate an Analysis (prov:Activity) from this plugin and the given parameters'''
a = Analysis()
a.plugin = self
if parameters:
a.params = parameters
return a
# More classes could be added programmatically
def _class_from_schema(name, schema=None, schema_file=None, base_classes=None):
def from_schema(name, schema=None, schema_file=None, base_classes=None):
base_classes = base_classes or []
base_classes.append(BaseModel)
attrs = {}
if schema:
attrs['schema'] = schema
elif schema_file:
attrs['schema_file'] = schema_file
else:
attrs['schema'] = name
name = "".join((name[0].upper(), name[1:]))
return BaseMeta(name, base_classes, attrs)
schema_file = schema_file or '{}.json'.format(name)
class_name = '{}{}'.format(name[0].upper(), name[1:])
if '/' not in 'schema_file':
schema_file = os.path.join(os.path.dirname(os.path.realpath(__file__)),
'schemas',
schema_file)
schema_path = 'file://' + schema_file
with open(schema_file) as f:
schema = json.load(f)
dct = {}
resolver = jsonschema.RefResolver(schema_path, schema)
dct['@type'] = name
dct['_schema_file'] = schema_file
dct['schema'] = schema
dct['_validator'] = jsonschema.Draft4Validator(schema, resolver=resolver)
newclass = type(class_name, tuple(base_classes), dct)
register(newclass, name)
return newclass
def _add_class_from_schema(*args, **kwargs):
generatedClass = _class_from_schema(*args, **kwargs)
def _add_from_schema(*args, **kwargs):
generatedClass = from_schema(*args, **kwargs)
globals()[generatedClass.__name__] = generatedClass
del generatedClass
for i in [
'analysis',
'emotion',
'emotionConversion',
'emotionConversionPlugin',
'emotionAnalysis',
'emotionModel',
'emotionPlugin',
'emotionSet',
'entry',
'help',
'plugin',
'plugins',
'response',
'results',
'sentiment',
'sentimentPlugin',
'suggestion',
]:
_add_from_schema(i)
_ErrorModel = from_schema('error')
class Error(SenpyMixin, Exception):
def __init__(self, message, *args, **kwargs):
super(Error, self).__init__(self, message, message)
self._error = _ErrorModel(message=message, *args, **kwargs)
self.message = message
def validate(self, obj=None):
self._error.validate()
def __getitem__(self, key):
return self._error[key]
def __setitem__(self, key, value):
self._error[key] = value
def __delitem__(self, key):
del self._error[key]
def __getattr__(self, key):
if key != '_error' and hasattr(self._error, key):
return getattr(self._error, key)
raise AttributeError(key)
def __setattr__(self, key, value):
if key != '_error':
return setattr(self._error, key, value)
else:
super(Error, self).__setattr__(key, value)
def __delattr__(self, key):
delattr(self._error, key)
def __str__(self):
return str(self.to_JSON(with_context=False))
register(Error, 'error')

File diff suppressed because it is too large Load Diff

Some files were not shown because too many files have changed in this diff Show More