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2 Commits

Author SHA1 Message Date
Daniel Suarez Souto
530982be62 Fixed some code problems 2018-09-28 11:36:53 +02:00
Daniel Suarez Souto
38b478890b Added maxSentiment plugin 2018-09-28 11:11:47 +02:00
169 changed files with 47155 additions and 21433 deletions

3
.gitignore vendored
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@@ -7,5 +7,4 @@ README.html
__pycache__
VERSION
Dockerfile-*
Dockerfile
senpy_data
Dockerfile

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@@ -4,130 +4,110 @@
# - 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
testpy310:
tags:
- docker
- make -e test-$PYTHON_VERSION
except:
- tags # Avoid unnecessary double testing
test-3.5:
<<: *test_definition
variables:
SENPY_STRICT: "true"
image: python:3.10
stage: test
script:
- pip install -r requirements.txt -r test-requirements.txt -r extra-requirements.txt
- python setup.py test
PYTHON_VERSION: "3.5"
push_pypi:
test-2.7:
<<: *test_definition
variables:
PYTHON_VERSION: "2.7"
.image: &image_definition
stage: push
script:
- make -e push-$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
latest-demo:
only:
refs:
- master
tags:
- docker
image: alpine/k8s:1.22.6
stage: deploy
environment: production
push-3.5:
<<: *image_definition
variables:
KUBECONFIG: "/kubeconfig"
# Same image as docker-latest
IMAGEWTAG: "${LATEST_IMAGE}"
KUBEAPP: "senpy"
PYTHON_VERSION: "3.5"
push-2.7:
<<: *image_definition
variables:
PYTHON_VERSION: "2.7"
push-latest:
<<: *image_definition
variables:
PYTHON_VERSION: latest
only:
- master
- triggers
- fix-makefiles
push-github:
stage: deploy
script:
- echo "${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

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@@ -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):

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@@ -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

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@@ -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

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@@ -29,7 +29,7 @@ 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 '$(IMAGEWTAG)-python$*' -f Dockerfile-$* .;
dev-%: ## Launch a specific development environment using docker (e.g. dev-2.7)
@docker start $(NAME)-dev$* || (\

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@@ -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

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@@ -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

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@@ -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

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@@ -6,20 +6,21 @@ 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
ONBUILD COPY . /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 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 WORKDIR /senpy-plugins/
ENTRYPOINT ["python", "-m", "senpy", "-f", "/senpy-plugins/", "--host", "0.0.0.0"]

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@@ -2,10 +2,9 @@ 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
graft senpy/templates
graft senpy/static
graft img
graft img

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@@ -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

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@@ -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

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@@ -1,25 +1,18 @@
.. image:: img/header.png
:width: 100%
:target: http://senpy.gsi.upm.es
:target: http://demos.gsi.dit.upm.es/senpy
.. 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/
.. 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
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@@ -0,0 +1,4 @@
import os
SERVER_PORT = os.environ.get("SERVER_PORT", 5000)
DEBUG = os.environ.get("DEBUG", True)

File diff suppressed because it is too large Load Diff

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@@ -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",
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" <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",
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" <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",
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" <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",
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" <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",
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" <span class=\"nt\">&quot;value&quot;</span><span class=\"p\">:</span> <span class=\"mf\">0.3107177974434612</span>\n",
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" <span class=\"p\">{</span>\n",
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" {\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
}

106
docs/SenpyClientUse.rst Normal file
View File

@@ -0,0 +1,106 @@
Client
======
Demo Endpoint
-------------
Import Client and send a request
.. code:: python
from senpy.client import Client
c = Client('http://latest.senpy.cluster.gsi.dit.upm.es/api')
r = c.analyse('I like Pizza', algorithm='sentiment140')
Print response
.. code:: python
for entry in r.entries:
print('{} -> {}'.format(entry['text'], entry['sentiments'][0]['marl:hasPolarity']))
.. parsed-literal::
I like Pizza -> marl:Positive
Obtain a list of available plugins
.. code:: python
for plugin in c.request('/plugins')['plugins']:
print(plugin['name'])
.. parsed-literal::
emoRand
rand
sentiment140
Local Endpoint
--------------
Run a docker container with Senpy image and default plugins
.. code::
docker run -ti --name 'SenpyEndpoint' -d -p 5000:5000 gsiupm/senpy:0.8.6 --host 0.0.0.0 --default-plugins
.. parsed-literal::
a0157cd98057072388bfebeed78a830da7cf0a796f4f1a3fd9188f9f2e5fe562
Import client and send a request to localhost
.. code:: python
c_local = Client('http://127.0.0.1:5000/api')
r = c_local.analyse('Hello world', algorithm='sentiment140')
Print response
.. code:: python
for entry in r.entries:
print('{} -> {}'.format(entry['text'], entry['sentiments'][0]['marl:hasPolarity']))
.. parsed-literal::
Hello world -> marl:Neutral
Obtain a list of available plugins deployed locally
.. code:: python
c_local.plugins().keys()
.. parsed-literal::
rand
sentiment140
emoRand
Stop the docker container
.. code:: python
!docker stop SenpyEndpoint
!docker rm SenpyEndpoint
.. parsed-literal::
SenpyEndpoint
SenpyEndpoint

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
[
{
"@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"
}
],
"prov:wasGeneratedBy": "prefix:Analysis_1562744784.8789825"
}
]
... rest of the document ...
{
"@type": "emotionSet",
"onyx:hasEmotion": {
"@type": "emotion",
"onyx:hasEmotionCategory": "emoml:big6anger"
},
"prov:wasGeneratedBy": "plugins/emoRand_0.1"
}
To get the equivalent of these emotions in Ekman's categories (i.e., Ekman's Big 6 in EmotionML), we'd do this:
To get these emotions in VAD space (FSRE dimensions 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
[
{
"@type": "EmotionSet",
"onyx:hasEmotion": [
... rest of the document ...
{
"@type": "Emotion",
"onyx:algorithmConfidence": 4.4979,
"onyx:hasEmotionCategory": "emoml:big6happiness"
"@type": "emotionSet",
"onyx:hasEmotion": {
"@type": "emotion",
"onyx:hasEmotionCategory": "emoml:big6anger"
},
"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"
}
],
"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"
},
"prov:wasGeneratedBy": "prefix:Analysis_1562745220.1570725"
}
]
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

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@@ -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

@@ -1,18 +1,19 @@
{
"@context": "http://mixedemotions-project.eu/ns/context.jsonld",
"@id": "me:Result1",
"@type": "results",
"activities": [ ],
"entries": [
{
"@id": "http://example.org#char=0,40",
"@type": [
"nif:RFC5147String",
"nif:Context"
],
"nif:beginIndex": 0,
"nif:endIndex": 40,
"nif:isString": "My favourite actress is Natalie Portman"
}
]
"@context": "http://mixedemotions-project.eu/ns/context.jsonld",
"@id": "http://example.com#NIFExample",
"@type": "results",
"analysis": [
],
"entries": [
{
"@id": "http://example.org#char=0,40",
"@type": [
"nif:RFC5147String",
"nif:Context"
],
"nif:beginIndex": 0,
"nif:endIndex": 40,
"nif:isString": "My favourite actress is Natalie Portman"
}
]
}

View File

@@ -1,100 +1,88 @@
{
"@context": "http://mixedemotions-project.eu/ns/context.jsonld",
"@id": "me:Result1",
"@type": "results",
"activities": [
"@context": "http://mixedemotions-project.eu/ns/context.jsonld",
"@id": "me:Result1",
"@type": "results",
"analysis": [
{
"@id": "me:SAnalysis1",
"@type": "marl:SentimentAnalysis",
"marl:maxPolarityValue": 1,
"marl:minPolarityValue": 0
},
{
"@id": "me:SgAnalysis1",
"@type": "me:SuggestionAnalysis"
},
{
"@id": "me:EmotionAnalysis1",
"@type": "me:EmotionAnalysis"
},
{
"@id": "me:NER1",
"@type": "me:NER"
}
],
"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": "_:SAnalysis1_Activity",
"@type": "marl:SentimentAnalysis",
"prov:wasAssociatedWith": "me:SentimentAnalysis",
"prov:used": [
{
"name": "marl:maxPolarityValue",
"prov:value": "1"
},
{
"name": "marl:minPolarityValue",
"prov:value": "0"
}
]
"@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": "_:SgAnalysis1_Activity",
"prov:wasAssociatedWith": "me:SgAnalysis1",
"@type": "me:SuggestionAnalysis"
},
{
"@id": "_:EmotionAnalysis1_Activity",
"@type": "me:EmotionAnalysis",
"prov:wasAssociatedWith": "me:EmotionAnalysis1"
},
{
"@id": "_:NER1_Activity",
"@type": "me:NER",
"prov:wasAssociatedWith": "me:EmotionNER1"
"@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"
}
],
"entries": [
],
"suggestions": [
{
"@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"
}
]
}
]
"@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

@@ -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/
: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
:target: https://badge.fury.io/py/senpy
.. 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:
:caption: Learn more about senpy:
:maxdepth: 2
senpy
demo
Quickstart.ipynb
installation
conversion
Evaluation.ipynb
apischema
development
publications
projects
senpy
installation
demo
usage
apischema
plugins
conversion
about

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@@ -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:
Alternatively, you can use the development version:
.. code:: bash
.. 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>`_.
git clone git@github.com:gsi-upm/senpy
cd senpy
pip install --user .
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:

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@@ -9,21 +9,20 @@ Lastly, it is also possible to add new plugins programmatically.
.. contents:: :local:
..
What is a plugin?
=================
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:
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.
- 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.
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.
The only limitation is that the name of each plugin needs to be unique.
Definition files
================
@@ -110,3 +109,5 @@ Now, in a file named ``helloworld.py``:
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>`__.

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@@ -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.

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@@ -1,18 +1,61 @@
F.A.Q.
======
Developing new plugins
----------------------
This document contains the minimum to get you started with developing new analysis plugin.
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.cluster.gsi.dit.upm.es/senpy/senpy-tutorial>`__
.. contents:: :local:
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.
What are annotations?
#####################
=====================
They are objects just like entries.
Senpy ships with several default annotations, including ``Sentiment`` and ``Emotion``.
Senpy ships with several default annotations, including: ``Sentiment``, ``Emotion``, ``EmotionSet``...jk bb
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.
@@ -22,33 +65,45 @@ Hence, two parts are necessary: 1) the code that will process the entry, and 2)
In practice, this is what a plugin looks like, tests included:
.. literalinclude:: ../example-plugins/rand_plugin.py
:emphasize-lines: 21-28
.. literalinclude:: ../senpy/plugins/example/rand_plugin.py
:emphasize-lines: 5-11
: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.
* A unique name for the class. In our example, Rand.
* 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.
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.
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`)
* Plugin definition files (see :doc:`advanced-plugins`)
How can I define additional parameters for my plugin?
#####################################################
Defining additional parameters
==============================
Your plugin may ask for additional parameters from users by using the attribute ``extra_params`` in your plugin definition.
Your plugin may ask for additional parameters from the users of the service 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.
@@ -69,16 +124,15 @@ It takes a dictionary, where the keys are the name of the argument/parameter, an
How should I load external data and files
#########################################
Loading 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:
Plugins have a convenience function `self.open` which will automatically prepend the data folder to relative paths:
.. code:: python
@@ -90,7 +144,7 @@ Plugins have a convenience function `self.open` which will automatically look fo
file_in_data = <FILE PATH>
file_in_sources = <FILE PATH>
def on activate(self):
def 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)
@@ -101,8 +155,8 @@ Plugins have a convenience function `self.open` which will automatically look fo
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?
#########################################
Docker image
============
Add the following dockerfile to your project to generate a docker image with your plugin:
@@ -133,7 +187,7 @@ And you can run it with:
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.
If the plugin uses non-source files (:ref:`loading 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.
@@ -150,15 +204,17 @@ Adding data to the image:
FROM gsiupm/senpy:1.0.1
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 chunker may split one entry into several.
@@ -166,7 +222,7 @@ On the other hand, a conversion plugin may leave out those entries that do not c
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:
@@ -200,7 +256,7 @@ 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.
@@ -236,8 +292,8 @@ This example ilustrate how to implement a plugin that accesses the Sentiment140
yield entry
How can I activate a DEBUG mode for my plugin?
###############################################
Can I activate a DEBUG mode for my plugin?
???????????????????????????????????????????
You can activate the DEBUG mode by the command-line tool using the option -d.
@@ -253,6 +309,6 @@ Additionally, with the ``--pdb`` option you will be dropped into a pdb post mort
python -m pdb yourplugin.py
Where can I find more code examples?
####################################
????????????????????????????????????
See: `<http://github.com/gsi-upm/senpy-plugins-community>`_.

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@@ -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.

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@@ -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).

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

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@@ -1,27 +1,54 @@
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.
Senpy is a framework for text analysis using Linked Data. There are three main applications of Senpy so far: sentiment and emotion analysis, user profiling and entity recoginition. Annotations and Services are compliant with NIF (NLP Interchange Format).
Senpy aims at providing a framework where analysis modules can be integrated easily as plugins, and providing a core functionality for managing tasks such as data validation, user interaction, formatting, logging, translation to linked data, etc.
The figure below summarizes the typical features in a text 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

@@ -1,18 +1,14 @@
Command line tool
=================
Basic usage
-----------
Server
======
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]
usage: senpy [-h] [--level logging_level] [--debug] [--default-plugins]
[--host HOST] [--port PORT] [--plugins-folder PLUGINS_FOLDER]
[--only-install] [--only-list] [--data-folder DATA_FOLDER]
[--threaded] [--version]
Run a Senpy server
@@ -20,24 +16,20 @@ The senpy server is launched via the `senpy` command:
-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
--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.
--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
--only-install, -i Do not run a server, only install plugin dependencies
--only-list, --list Do not run a server, only list plugins found
--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
--threaded Run a threaded server
--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).
@@ -48,9 +40,9 @@ Let's run senpy with the default plugins:
.. code:: bash
senpy -f .
senpy -f . --default-plugins
Now open your browser and go to `http://localhost:5000 <http://localhost:5000>`_, where you should be greeted by the senpy playground:
Now go to `http://localhost:5000 <http://localhost:5000>`_, you should be greeted by the senpy playground:
.. image:: senpy-playground.png
:width: 100%
@@ -59,9 +51,9 @@ Now open your browser and go to `http://localhost:5000 <http://localhost:5000>`_
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).
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.
@@ -72,14 +64,3 @@ For instance, to accept connections on port 6000 on any interface:
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

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

@@ -13,9 +13,9 @@ 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/
[2] Marl Ontology Specification, available at http://www.gsi.dit.upm.es/ontologies/marl/
[3] Onyx Ontology Specification, available at http://www.gsi.upm.es/ontologies/onyx/
[3] Onyx Ontology Specification, available at http://www.gsi.dit.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).

View File

@@ -1,6 +1,6 @@
This is a collection of plugins that exemplify certain aspects of plugin development with senpy.
The first series of plugins are the `basic` ones.
The first series of plugins 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:
@@ -19,5 +19,5 @@ In rest of the plugins show advanced topics:
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
Additioanlly, for an example of stand-alone plugin that can be tested and deployed with docker, take a look at: lab.cluster.gsi.dit.upm.es/senpy/plugin-example
bbm

View File

@@ -1,19 +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 senpy import AnalysisPlugin
import multiprocessing
@@ -27,7 +11,7 @@ class Async(AnalysisPlugin):
'''An example of an asynchronous module'''
author = '@balkian'
version = '0.2'
sync = False
async = True
def _do_async(self, num_processes):
pool = multiprocessing.Pool(processes=num_processes)

View File

@@ -1,21 +1,5 @@
#!/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.
#
# coding: utf-8
emoticons = {
'pos': [':)', ':]', '=)', ':D'],
@@ -23,19 +7,17 @@ emoticons = {
}
emojis = {
'pos': [u'😁', u'😂', u'😃', u'😄', u'😆', u'😅', u'😄', u'😍'],
'neg': [u'😢', u'😡', u'😠', u'😞', u'😖', u'😔', u'😓', u'😒']
'pos': ['😁', '😂', '😃', '😄', '😆', '😅', '😄' '😍'],
'neg': ['😢', '😡', '😠', '😞', '😖', '😔', '😓', '😒']
}
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

@@ -1,20 +1,5 @@
#!/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.
#
# coding: utf-8
from senpy import easy_test, models, plugins
@@ -33,13 +18,13 @@ class BasicAnalyseEntry(plugins.SentimentPlugin):
'default': 'marl:Neutral'
}
def analyse_entry(self, entry, activity):
def analyse_entry(self, entry, params):
polarity = basic.get_polarity(entry.text)
polarity = self.mappings.get(polarity, self.mappings['default'])
s = models.Sentiment(marl__hasPolarity=polarity)
s.prov(activity)
s.prov(self)
entry.sentiments.append(s)
yield entry

View File

@@ -1,20 +1,5 @@
#!/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.
#
# coding: utf-8
from senpy import easy_test, SentimentBox
@@ -27,13 +12,15 @@ class BasicBox(SentimentBox):
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]
mappings = {
'pos': 'marl:Positive',
'neg': 'marl:Negative',
'default': 'marl:Neutral'
}
def predict_one(self, input):
output = basic.get_polarity(input)
return self.mappings.get(output, self.mappings['default'])
test_cases = [{
'input': 'Hello :)',

View File

@@ -1,52 +1,37 @@
#!/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.
#
# coding: utf-8
from senpy import easy_test, SentimentBox
from senpy import easy_test, SentimentBox, MappingMixin
import basic
class Basic(SentimentBox):
class Basic(MappingMixin, 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]
mappings = {
'pos': 'marl:Positive',
'neg': 'marl:Negative',
'default': 'marl:Neutral'
}
def predict_one(self, input):
return basic.get_polarity(input)
test_cases = [{
'input': u'Hello :)',
'input': 'Hello :)',
'polarity': 'marl:Positive'
}, {
'input': u'So sad :(',
'input': 'So sad :(',
'polarity': 'marl:Negative'
}, {
'input': u'Yay! Emojis 😁',
'input': 'Yay! Emojis 😁',
'polarity': 'marl:Positive'
}, {
'input': u'But no emoticons 😢',
'input': 'But no emoticons 😢',
'polarity': 'marl:Negative'
}]

View File

@@ -1,21 +1,5 @@
#!/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.
#
# coding: utf-8
from senpy import easy_test, models, plugins
@@ -32,7 +16,7 @@ class Dictionary(plugins.SentimentPlugin):
mappings = {'pos': 'marl:Positive', 'neg': 'marl:Negative'}
def analyse_entry(self, entry, *args, **kwargs):
def analyse_entry(self, entry, params):
polarity = basic.get_polarity(entry.text, self.dictionaries)
if polarity in self.mappings:
polarity = self.mappings[polarity]

View File

@@ -1,19 +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 senpy import AnalysisPlugin, easy

View File

@@ -1,19 +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 senpy import AnalysisPlugin, easy

View File

@@ -1,19 +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.
#
import noop
from senpy.plugins import SentimentPlugin

View File

@@ -1,4 +1,3 @@
module: mynoop
optional: true
requirements:
- noop

View File

@@ -1,21 +1,5 @@
#!/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.
#
# coding: utf-8
from senpy import easy_test, models, plugins
@@ -41,8 +25,7 @@ class ParameterizedDictionary(plugins.SentimentPlugin):
}
}
def analyse_entry(self, entry, activity):
params = activity.params
def analyse_entry(self, entry, params):
positive_words = params['positive-words'].split(',')
negative_words = params['negative-words'].split(',')
dictionary = {
@@ -52,7 +35,7 @@ class ParameterizedDictionary(plugins.SentimentPlugin):
polarity = basic.get_polarity(entry.text, [dictionary])
s = models.Sentiment(marl__hasPolarity=polarity)
s.prov(activity)
s.prov(self)
entry.sentiments.append(s)
yield entry

View File

@@ -1,19 +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.
#
'''
Create a dummy dataset.
Messages with a happy emoticon are labelled positive

View File

@@ -1,19 +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 sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
@@ -31,7 +15,7 @@ pipeline = Pipeline([('cv', count_vec),
('clf', clf3)])
pipeline.fit(X_train, y_train)
print('Feature names: {}'.format(count_vec.get_feature_names_out()))
print('Feature names: {}'.format(count_vec.get_feature_names()))
print('Class count: {}'.format(clf3.class_count_))

View File

@@ -1,36 +1,25 @@
#
# 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 senpy import SentimentBox, MappingMixin, easy_test
from mypipeline import pipeline
class PipelineSentiment(SentimentBox):
'''This is a pipeline plugin that wraps a classifier defined in another module
(mypipeline).'''
class PipelineSentiment(MappingMixin, 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]
mappings = {
1: 'marl:Positive',
-1: 'marl:Negative'
}
def predict_one(self, input):
return pipeline.predict([input, ])[0]
test_cases = [
{

View File

@@ -1,19 +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 senpy.plugins import AnalysisPlugin
from time import sleep

View File

@@ -1,6 +1 @@
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
gsitk

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
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

@@ -7,9 +7,9 @@ future
jsonschema
jsonref
PyYAML
rdflib==6.1.1
rdflib
rdflib-jsonld
numpy
scipy
scikit-learn>=0.20
scikit-learn
responses
jmespath

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
"""

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.
@@ -22,8 +23,6 @@ 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
@@ -41,19 +40,8 @@ def main():
'-l',
metavar='logging_level',
type=str,
default="INFO",
default="WARN",
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 +49,10 @@ 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,
@@ -80,24 +68,19 @@ def main():
'--plugins-folder',
'-f',
type=str,
action='append',
default='.',
help='Where to look for plugins.')
parser.add_argument(
'--install',
'--only-install',
'-i',
action='store_true',
default=False,
help='Install plugin dependencies before running.')
help='Do not run a server, only install plugin dependencies')
parser.add_argument(
'--dependencies',
'--only-test',
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.')
help='Do not run a server, just test all plugins')
parser.add_argument(
'--test',
'-t',
@@ -105,10 +88,11 @@ def main():
default=False,
help='Test all plugins before launching the server')
parser.add_argument(
'--no-run',
'--only-list',
'--list',
action='store_true',
default=False,
help='Do not launch the server.')
help='Do not run a server, only list plugins found')
parser.add_argument(
'--data-folder',
'--data',
@@ -116,10 +100,10 @@ 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')
'--threaded',
action='store_false',
default=True,
help='Run a threaded server')
parser.add_argument(
'--no-deps',
'-n',
@@ -138,106 +122,48 @@ def main():
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()
print('Senpy version {}'.format(senpy.__version__))
print(sys.version)
if args.version:
print('Senpy version {}'.format(senpy.__version__))
print(sys.version)
exit(1)
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_list:
plugins = sp.plugins()
maxname = max(len(x.name) for x in plugins)
maxversion = max(len(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))
return
sp.activate_all(sync=True)
if sp.strict:
inactive = sp.plugins(is_activated=False)
assert not inactive
if not args.no_deps:
sp.install_deps()
if args.only_install:
return
sp.activate_all(allow_fail=args.allow_fail)
if args.test or args.only_test:
easy_test(sp.plugins(), debug=args.debug)
if args.only_test:
return
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,
debug=app.debug)
except KeyboardInterrupt:
print('Bye!')
app.run(args.host,
args.port,
threaded=args.threaded,
debug=app.debug)
sp.deactivate_all()

View File

@@ -1,51 +1,29 @@
#
# 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"],
"aliases": ["expanded"],
"options": boolean,
"required": True,
"default": False
},
"with-parameters": {
"with_parameters": {
"aliases": ['withparameters',
'with_parameters'],
"description": "include initial parameters in the response",
'with-parameters'],
"options": boolean,
"default": False,
"required": True
@@ -54,67 +32,9 @@ API_PARAMS = {
"@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
"""
},
"help": {
"@id": "help",
"description": "Show additional help to know more about the possible parameters",
@@ -123,41 +43,14 @@ For more information, see: https://jmespath.org
"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"
@@ -167,10 +60,9 @@ Examples: `wna:liking` and `emoml:big6`.
EVAL_PARAMS = {
"algorithm": {
"aliases": ["plug", "p", "plugins", "algorithms", 'algo', 'a', 'plugin'],
"description": "Plugins to evaluate",
"description": "Plugins to be evaluated",
"required": True,
"help": "See activated plugins in /plugins",
"processor": API_PARAMS['algorithm']['processor']
"help": "See activated plugins in /plugins"
},
"dataset": {
"aliases": ["datasets", "data", "d"],
@@ -181,19 +73,18 @@ EVAL_PARAMS = {
}
PLUGINS_PARAMS = {
"plugin-type": {
"plugin_type": {
"@id": "pluginType",
"description": 'What kind of plugins to list',
"aliases": ["pluginType", "plugin_type"],
"aliases": ["pluginType"],
"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
@@ -201,8 +92,8 @@ WEB_PARAMS = {
}
CLI_PARAMS = {
"plugin-folder": {
"aliases": ["folder", "plugin_folder"],
"plugin_folder": {
"aliases": ["folder"],
"required": True,
"default": "."
},
@@ -217,7 +108,6 @@ NIF_PARAMS = {
},
"intype": {
"@id": "intype",
"description": "input type",
"aliases": ["t"],
"required": False,
"default": "direct",
@@ -225,7 +115,6 @@ NIF_PARAMS = {
},
"informat": {
"@id": "informat",
"description": "input format",
"aliases": ["f"],
"required": False,
"default": "text",
@@ -233,20 +122,17 @@ NIF_PARAMS = {
},
"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",
@@ -254,15 +140,6 @@ NIF_PARAMS = {
}
}
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:
@@ -277,7 +154,7 @@ def parse_params(indict, *specs):
if alias in indict and alias != param:
outdict[param] = indict[alias]
del outdict[alias]
break
continue
if param not in outdict:
if "default" in options:
# We assume the default is correct
@@ -285,11 +162,9 @@ def parse_params(indict, *specs):
elif options.get("required", False):
wrong_params[param] = spec[param]
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', '']
outdict[param] = outdict[param] in [None, True, 'true', '1']
elif outdict[param] not in options["options"]:
wrong_params[param] = spec[param]
if wrong_params:
@@ -300,126 +175,31 @@ def parse_params(indict, *specs):
parameters=outdict,
errors=wrong_params)
raise message
if 'algorithm' in outdict and not isinstance(outdict['algorithm'], list):
outdict['algorithm'] = list(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 parse_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'],
id='#') # Use @base
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']))
raise NotImplementedError('Informat {} is not implemented'.format(params['informat']))
results.parameters = params
return results

View File

@@ -1,20 +1,19 @@
#!/usr/bin/python
# -*- coding: utf-8 -*-
# Copyright 2014 J. Fernando Sánchez Rada - Grupo de Sistemas Inteligentes
# DIT, UPM
#
# 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
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# 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.
#
"""
Blueprints for Senpy
"""
@@ -25,8 +24,6 @@ from . import api
from .version import __version__
from functools import wraps
from .gsitk_compat import GSITK_AVAILABLE, datasets
import logging
import json
import base64
@@ -66,44 +63,44 @@ def get_params(req):
return indict
def encode_url(url=None):
def encoded_url(url=None, base=None):
code = ''
if not url:
url = request.parameters.get('prefix', request.full_path[1:] + '#')
return code or base64.urlsafe_b64encode(url.encode()).decode()
if request.method == 'GET':
url = request.full_path[1:] # Remove the first slash
else:
hash(frozenset(request.form.params().items()))
code = 'hash:{}'.format(hash)
code = 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/'
if base:
return base + code
return url_for('api.decode', code=code, _external=True)
def decoded_url(code, base=None):
path = base64.urlsafe_b64decode(code.encode()).decode()
if path[:4] == 'http':
return path
if code.startswith('hash:'):
raise Exception('Can not decode a URL for a POST request')
base = base or request.url_root
path = base64.urlsafe_b64decode(code.encode()).decode()
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
ev = str(get_params(request).get('evaluation', False))
evaluation_enabled = ev.lower() not in ['false', 'no', 'none']
return render_template("index.html",
evaluation=evaluation_enabled,
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['@vocab'] = url_for('ns.index', _external=True)
context['endpoint'] = url_for('api.api_root', _external=True)
return jsonify({"@context": context})
@@ -133,59 +130,26 @@ def schema(schema="definitions"):
def basic_api(f):
default_params = {
'in-headers': False,
'inHeaders': False,
'expanded-jsonld': False,
'outformat': None,
'with-parameters': True,
'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))
logger.info('Getting request: {}'.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
try:
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)
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)
return response.flask(
in_headers=params['in-headers'],
headers=headers,
prefix=prefix or url_for_code(code),
base=prefix,
context_uri=url_for('api.context',
code=code,
_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
@@ -195,52 +159,48 @@ def basic_api(f):
response = ex
response.parameters = raw_params
logger.exception(ex)
return response.flask(
outformat=outformat,
expanded=params['expanded-jsonld'],
verbose=params.get('verbose', True),
)
if 'parameters' in response and not params['with_parameters']:
del response.parameters
logger.info('Response: {}'.format(response))
mime = request.accept_mimetypes\
.best_match(MIMETYPES.keys(),
DEFAULT_MIMETYPE)
mimeformat = MIMETYPES.get(mime, DEFAULT_FORMAT)
outformat = params['outformat'] or mimeformat
return response.flask(
in_headers=params['inHeaders'],
headers=headers,
prefix=params.get('prefix', encoded_url()),
context_uri=url_for('api.context',
entity=type(response).__name__,
_external=True),
outformat=outformat,
expanded=params['expanded-jsonld'])
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('/', defaults={'plugin': None}, methods=['POST', 'GET'])
@api_blueprint.route('/<path:plugin>', 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(plugin):
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)
dic = dict(api.API_PARAMS, **api.NIF_PARAMS)
response = Help(valid_parameters=dic)
return response
req = api.parse_call(request.parameters)
analyses = api.parse_analyses(req.parameters, plugins)
results = current_app.senpy.analyse(req, analyses)
return results
if plugin:
plugin = plugin.replace('+', '/')
plugin = plugin.split('/')
req.parameters['algorithm'] = plugin
return current_app.senpy.analyse(req)
@api_blueprint.route('/evaluate', methods=['POST', 'GET'], strict_slashes=False)
@api_blueprint.route('/evaluate/', methods=['POST', 'GET'])
@basic_api
def evaluate():
if request.parameters['help']:
@@ -253,26 +213,28 @@ def evaluate():
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))
ptype = params.get('plugin_type')
plugins = list(sp.plugins(plugin_type=ptype))
dic = Plugins(plugins=plugins)
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):
sp = current_app.senpy
return sp.get_plugin(plugin)
@api_blueprint.route('/datasets', methods=['POST', 'GET'], strict_slashes=False)
@api_blueprint.route('/datasets/', methods=['POST', 'GET'])
@basic_api
def get_datasets():
def datasets():
sp = current_app.senpy
datasets = sp.datasets
dic = Datasets(datasets=list(datasets.values()))
return dic

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
@@ -44,14 +27,14 @@ 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)
plugin_folder = params['plugin_folder']
default_plugins = params.get('default-plugins', False)
sp = Senpy(default_plugins=default_plugins, plugin_folder=plugin_folder)
request = api.parse_call(params)
algos = sp.get_plugins(request.parameters.get('algorithm', None))
algos = request.parameters.get('algorithm', None)
if algos:
for algo in algos:
sp.activate_plugin(algo.name)
sp.activate_plugin(algo)
else:
sp.activate_all()
res = sp.analyse(request)
@@ -65,7 +48,7 @@ def main():
res = main_function(sys.argv[1:])
print(res.serialize())
except Error as err:
print(err.serialize(), file=sys.stderr)
print(err.serialize())
sys.exit(2)

View File

@@ -1,19 +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.
#
import requests
import logging
from . import models
@@ -41,11 +25,7 @@ class Client(object):
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:
response = requests.request(method=method, url=url, params=params)
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.
@@ -20,10 +5,9 @@ It orchestrates plugin (de)activation and analysis.
from future import standard_library
standard_library.install_aliases()
from . import config
from . import plugins, api
from .plugins import Plugin, evaluate
from .models import Error, AggregatedEvaluation
from .plugins import AnalysisPlugin
from .blueprints import api_blueprint, demo_blueprint, ns_blueprint
from threading import Thread
@@ -33,6 +17,7 @@ import copy
import errno
import logging
from . import gsitk_compat
logger = logging.getLogger(__name__)
@@ -40,16 +25,12 @@ logger = logging.getLogger(__name__)
class Senpy(object):
""" Default Senpy extension for Flask """
def __init__(self,
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:
@@ -61,8 +42,6 @@ class Senpy(object):
raise
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)
@@ -71,12 +50,11 @@ class Senpy(object):
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
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 """
@@ -97,55 +75,31 @@ class Senpy(object):
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):
def plugins(self, **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)
return list(plugins.pfilter(self._plugins, **kwargs))
def get_plugin(self, name, default=None):
if name == 'default':
return self.default_plugin
elif name == 'conversion':
return None
plugin = name.lower()
if plugin in self._plugins:
return self._plugins[plugin]
if name.lower() in self._plugins:
return self._plugins[name.lower()]
results = self.plugins(id='endpoint:plugins/{}'.format(name))
results = self.plugins(id='endpoint:plugins/{}'.format(name.lower()),
plugin_type=None)
if results:
return results[0]
if not results:
return Error(message="Plugin not found", status=404)
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))
@property
def analysis_plugins(self):
""" Return only the analysis plugins """
return self.plugins(plugin_type='analysisPlugin')
def add_folder(self, folder, from_root=False):
""" Find plugins in this folder and add them to this instance """
@@ -154,56 +108,135 @@ class Senpy(object):
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)
data_folder=self.data_folder)
for plugin in new_plugins:
self.add_plugin(plugin)
else:
raise AttributeError("Not a folder or does not exist: %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:
algo = algo.lower()
if algo not in self._plugins:
msg = ("The algorithm '{}' is not valid\n"
"Valid algorithms: {}").format(algo,
self._plugins.keys())
logger.debug(msg)
raise Error(
status=404,
message=msg)
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]
specific_params = api.parse_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())
plugins.install_deps(*self.plugins())
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))
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)
logger.debug("Returning analysis result: {}".format(results))
results.analysis = [i['plugin'].id for i in results.analysis]
return results
def convert_emotions(self, resp, analyses):
def _get_datasets(self, request):
if not self.datasets:
raise Error(
status=404,
message=("No datasets found."
" Please verify DatasetManager"))
datasets_name = request.parameters.get('dataset', None).split(',')
for dataset in datasets_name:
if dataset not in self.datasets:
logger.debug(("The dataset '{}' is not valid\n"
"Valid datasets: {}").format(dataset,
self.datasets.keys()))
raise Error(
status=404,
message="The dataset '{}' is not valid".format(dataset))
dm = gsitk_compat.DatasetManager()
datasets = dm.prepare_datasets(datasets_name)
return datasets
@property
def datasets(self):
self._dataset_list = {}
dm = gsitk_compat.DatasetManager()
for item in dm.get_datasets():
for key in item:
if key in self._dataset_list:
continue
properties = item[key]
properties['@id'] = key
self._dataset_list[key] = properties
return self._dataset_list
def evaluate(self, params):
logger.debug("evaluating request: {}".format(params))
results = AggregatedEvaluation()
results.parameters = params
datasets = self._get_datasets(results)
plugins = self._get_plugins(results)
for eval in evaluate(plugins, datasets):
results.evaluations.append(eval)
if 'with_parameters' not in results.parameters:
del results.parameters
logger.debug("Returning evaluation result: {}".format(results))
return results
def _conversion_candidates(self, fromModel, toModel):
candidates = self.plugins(plugin_type='emotionConversionPlugin')
for candidate in candidates:
for pair in candidate.onyx__doesConversion:
logging.debug(pair)
if pair['onyx:conversionFrom'] == fromModel \
and pair['onyx:conversionTo'] == toModel:
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
@@ -211,125 +244,52 @@ 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)),
status=404)
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()
candidates = self.plugins(plugin_type='analysisPlugin',
is_activated=True)
if len(candidates) > 0:
self._default = candidates[0]
else:
@@ -339,7 +299,7 @@ class Senpy(object):
@default_plugin.setter
def default_plugin(self, value):
if isinstance(value, plugins.Plugin):
if isinstance(value, Plugin):
if not value.is_activated:
raise AttributeError('The default plugin has to be activated.')
self._default = value
@@ -347,13 +307,13 @@ class Senpy(object):
else:
self._default = self._plugins[value.lower()]
def activate_all(self, sync=True):
def activate_all(self, sync=True, allow_fail=False):
ps = []
for plug in self._plugins.keys():
try:
self.activate_plugin(plug, sync=sync)
except Exception as ex:
if self.strict:
if not allow_fail:
raise
logger.error('Could not activate {}: {}'.format(plug, ex))
return ps
@@ -364,21 +324,23 @@ class Senpy(object):
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/deactivate 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}")
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
plugin.activate()
msg = "Plugin activated: {}".format(plugin.name)
logger.info(msg)
success = True
self._set_active(plugin, success)
return success
def activate_plugin(self, plugin_name, sync=True):
plugin_name = plugin_name.lower()
@@ -389,8 +351,7 @@ class Senpy(object):
logger.info("Activating plugin: {}".format(plugin.name))
if sync or not getattr(plugin, 'async', True) or getattr(
plugin, 'sync', False):
if sync or not getattr(plugin, 'async', True):
return self._activate(plugin)
else:
th = Thread(target=partial(self._activate, plugin))
@@ -401,7 +362,7 @@ class Senpy(object):
with plugin._lock:
if not plugin.is_activated:
return
plugin._deactivate()
plugin.deactivate()
logger.info("Plugin deactivated: {}".format(plugin.name))
def deactivate_plugin(self, plugin_name, sync=True):
@@ -411,11 +372,12 @@ class Senpy(object):
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 not getattr(plugin, 'async', True):
self._deactivate(plugin)
else:
th = Thread(target=plugin.deactivate)
th = Thread(target=partial(self._deactivate, plugin))
th.start()
return th

View File

@@ -1,23 +1,4 @@
#
# 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__)
@@ -31,37 +12,12 @@ def raise_exception(*args, **kwargs):
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 gsitk.evaluation.evaluation import Evaluation as Eval
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)
modules = locals()
except ImportError:
logger.warn(IMPORTMSG)
GSITK_AVAILABLE = False
GSITK_VERSION = ()
DatasetManager = Eval = Pipeline = prepare = raise_exception
datasets = {}
DatasetManager = Eval = Pipeline = raise_exception

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.
#
'''
Meta-programming for the models.
'''
@@ -23,8 +8,7 @@ import inspect
import copy
from abc import ABCMeta
from collections import namedtuple
from collections.abc import MutableMapping
from collections import MutableMapping, namedtuple
class BaseMeta(ABCMeta):
@@ -50,7 +34,6 @@ class BaseMeta(ABCMeta):
def __new__(mcs, name, bases, attrs, **kwargs):
register_afterwards = False
defaults = {}
aliases = {}
attrs = mcs.expand_with_schema(name, attrs)
if 'schema' in attrs:
@@ -58,21 +41,17 @@ class BaseMeta(ABCMeta):
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
fget, fset, fdel = make_property(info[i].indict)
rest[i] = property(fget=fget, fset=fset, fdel=fdel)
else:
defaults[i] = info[i]
rest['_defaults'] = defaults
rest['_aliases'] = aliases
cls = super(BaseMeta, mcs).__new__(mcs, name, tuple(bases), rest)
@@ -106,8 +85,7 @@ class BaseMeta(ABCMeta):
schema = json.load(f)
resolver = jsonschema.RefResolver(schema_path, schema)
if '@type' not in attrs:
attrs['@type'] = name
attrs['@type'] = "".join((name[0].lower(), name[1:]))
attrs['_schema_file'] = schema_file
attrs['schema'] = schema
attrs['_validator'] = jsonschema.Draft4Validator(schema, resolver=resolver)
@@ -161,11 +139,9 @@ class BaseMeta(ABCMeta):
return temp
def make_property(key, default=None):
def make_property(key):
def fget(self):
if default:
return self.get(key, copy.copy(default))
return self[key]
def fdel(self):
@@ -191,7 +167,7 @@ class CustomDict(MutableMapping, object):
'''
_defaults = {}
_aliases = {'id': '@id'}
_map_attr_key = {'id': '@id'}
def __init__(self, *args, **kwargs):
super(CustomDict, self).__init__()
@@ -200,13 +176,13 @@ class CustomDict(MutableMapping, object):
for arg in args:
self.update(arg)
for k, v in kwargs.items():
self[k] = v
self[self._attr_to_key(k)] = v
return self
def serializable(self, **kwargs):
def serializable(self):
def ser_or_down(item):
if hasattr(item, 'serializable'):
return item.serializable(**kwargs)
return item.serializable()
elif isinstance(item, dict):
temp = dict()
for kp in item:
@@ -218,9 +194,10 @@ class CustomDict(MutableMapping, object):
else:
return item
return ser_or_down(self.as_dict(**kwargs))
return ser_or_down(self.as_dict())
def __getitem__(self, key):
key = self._key_to_attr(key)
return self.__dict__[key]
def __setitem__(self, key, value):
@@ -228,23 +205,9 @@ class CustomDict(MutableMapping, object):
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 as_dict(self):
return {self._attr_to_key(k): v for k, v in self.__dict__.items()
if not self._internal_key(k)}
def __iter__(self):
return (k for k in self.__dict__ if not self._internal_key(k))
@@ -252,52 +215,43 @@ class CustomDict(MutableMapping, object):
def __len__(self):
return len(self.__dict__)
def __delitem__(self, key):
del self.__dict__[key]
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)
key = self._map_attr_key.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)
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))
try:
return self.__dict__[self._attr_to_key(key)]
except KeyError:
raise AttributeError
@staticmethod
def _internal_key(key):
return key[0] == '_'
def __str__(self):
return json.dumps(self.serializable(), sort_keys=True, indent=4)
return str(self.serializable())
def __repr__(self):
return json.dumps(self.serializable(), sort_keys=True, indent=4)
return str(self.serializable())
_Alias = namedtuple('Alias', ['indict', 'default'])
_Alias = namedtuple('Alias', 'indict')
def alias(key, default=None):
return _Alias(key, default)
def alias(key):
return _Alias(key)

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.
@@ -27,8 +12,6 @@ standard_library.install_aliases()
from future.utils import with_metaclass
from past.builtins import basestring
from jinja2 import Environment, BaseLoader
import time
import copy
import json
@@ -38,7 +21,6 @@ from flask import Response as FlaskResponse
from pyld import jsonld
import logging
import jmespath
logging.getLogger('rdflib').setLevel(logging.WARN)
logger = logging.getLogger(__name__)
@@ -49,9 +31,8 @@ from rdflib import Graph
from .meta import BaseMeta, CustomDict, alias
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):
@@ -140,21 +121,24 @@ class BaseModel(with_metaclass(BaseMeta, CustomDict)):
'''
# schema_file = DEFINITIONS_FILE
schema_file = DEFINITIONS_FILE
_context = base_context["@context"]
def __init__(self, *args, **kwargs):
auto_id = kwargs.pop('_auto_id', False)
auto_id = kwargs.pop('_auto_id', True)
super(BaseModel, self).__init__(*args, **kwargs)
if auto_id:
self.id
if '@type' not in self:
logger.warn('Created an instance of an unknown model')
@property
def id(self):
if '@id' not in self:
self['@id'] = 'prefix:{}_{}'.format(type(self).__name__, time.time())
self['@id'] = '_:{}_{}'.format(type(self).__name__, time.time())
return self['@id']
@id.setter
@@ -190,33 +174,24 @@ class BaseModel(with_metaclass(BaseMeta, CustomDict)):
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':
content = json.dumps(js, indent=2, sort_keys=True)
def serialize(self, format='json-ld', with_mime=False, **kwargs):
js = self.jsonld(**kwargs)
content = json.dumps(js, indent=2, sort_keys=True)
if format == 'json-ld':
mimetype = "application/json"
elif format in ['turtle', 'ntriples']:
content = json.dumps(js, indent=2, sort_keys=True)
logger.debug(js)
context = [self._context, {'prefix': prefix, '@base': prefix}]
base = kwargs.get('prefix')
g = Graph().parse(
data=content,
format='json-ld',
prefix=prefix,
context=context)
base=base,
context=[self._context,
{'@base': base}])
logger.debug(
'Parsing with prefix: {}'.format(kwargs.get('prefix')))
content = g.serialize(format=format,
prefix=prefix)
base=base).decode('utf-8')
mimetype = 'text/{}'.format(format)
else:
raise Error('Unknown outformat: {}'.format(format))
@@ -229,25 +204,14 @@ class BaseModel(with_metaclass(BaseMeta, CustomDict)):
with_context=False,
context_uri=None,
prefix=None,
base=None,
expanded=False,
**kwargs):
expanded=False):
result = self.serializable(**kwargs)
result = self.serializable()
if expanded:
result = jsonld.expand(
result,
options={
'expandContext': [
self._context,
{
'prefix': prefix,
'endpoint': prefix
}
]
}
)[0]
result, options={'base': prefix,
'expandContext': self._context})[0]
if not with_context:
try:
del result['@context']
@@ -275,7 +239,7 @@ def subtypes():
return BaseMeta._subtypes
def from_dict(indict, cls=None, warn=True):
def from_dict(indict, cls=None):
if not cls:
target = indict.get('@type', None)
cls = BaseModel
@@ -283,10 +247,6 @@ def from_dict(indict, cls=None, warn=True):
cls = subtypes()[target]
except KeyError:
pass
if cls == BaseModel and warn:
logger.warning('Created an instance of an unknown model')
outdict = dict()
for k, v in indict.items():
if k == '@context':
@@ -306,24 +266,22 @@ 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')
polarityValue = alias('marl:hasPolarityValue')
class Error(BaseModel, Exception):
@@ -343,173 +301,7 @@ class Error(BaseModel, Exception):
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
# Add the remaining schemas programmatically
def _class_from_schema(name, schema=None, schema_file=None, base_classes=None):
base_classes = base_classes or []
@@ -529,3 +321,31 @@ def _add_class_from_schema(*args, **kwargs):
generatedClass = _class_from_schema(*args, **kwargs)
globals()[generatedClass.__name__] = generatedClass
del generatedClass
for i in [
'aggregatedEvaluation',
'analysis',
'dataset',
'datasets',
'emotion',
'emotionConversion',
'emotionConversionPlugin',
'emotionAnalysis',
'emotionModel',
'emotionPlugin',
'emotionSet',
'evaluation',
'entity',
'help',
'metric',
'plugin',
'plugins',
'response',
'results',
'sentimentPlugin',
'suggestion',
'topic',
]:
_add_class_from_schema(i)

File diff suppressed because it is too large Load Diff

View File

@@ -1,19 +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 senpy.plugins import EmotionConversionPlugin
from senpy.models import EmotionSet, Emotion, Error
@@ -101,13 +85,7 @@ class CentroidConversion(EmotionConversionPlugin):
def distance(centroid):
return sum(distance_k(centroid, original, k) for k in dimensions)
distances = {k: distance(centroids[k]) for k in centroids}
logger.debug('Converting %s', original)
logger.debug('Centroids: %s', centroids)
logger.debug('Distances: %s', distances)
emotion = min(distances, key=lambda x: distances[x])
emotion = min(centroids, key=lambda x: distance(centroids[x]))
result = Emotion(onyx__hasEmotionCategory=emotion)
result.onyx__algorithmConfidence = distance(centroids[emotion])
@@ -125,9 +103,7 @@ class CentroidConversion(EmotionConversionPlugin):
for i in emotionSet.onyx__hasEmotion:
e.onyx__hasEmotion.append(self._backwards_conversion(i))
else:
raise Error('EMOTION MODEL NOT KNOWN. '
'Cannot convert from {} to {}'.format(fromModel,
toModel))
raise Error('EMOTION MODEL NOT KNOWN')
yield e
def test(self, info=None):

View File

@@ -1,6 +1,6 @@
---
name: Ekman2FSRE
module: senpy.plugins.postprocessing.emotion.centroids
module: senpy.plugins.conversion.emotion.centroids
description: Plugin to convert emotion sets from Ekman to VAD
version: 0.2
# No need to specify onyx:doesConversion because centroids.py adds it automatically from centroids_direction

View File

@@ -1,6 +1,6 @@
---
name: Ekman2PAD
module: senpy.plugins.postprocessing.emotion.centroids
module: senpy.plugins.conversion.emotion.centroids
description: Plugin to convert emotion sets from Ekman to VAD
version: 0.2
# No need to specify onyx:doesConversion because centroids.py adds it automatically from centroids_direction
@@ -9,30 +9,30 @@ centroids:
anger:
A: 6.95
D: 5.1
P: 2.7
V: 2.7
disgust:
A: 5.3
D: 8.05
P: 2.7
V: 2.7
fear:
A: 6.5
D: 3.6
P: 3.2
V: 3.2
happiness:
A: 7.22
D: 6.28
P: 8.6
V: 8.6
sadness:
A: 5.21
D: 2.82
P: 2.21
V: 2.21
centroids_direction:
- emoml:big6
- emoml:pad-dimensions
- emoml:pad
aliases: # These are aliases for any key in the centroid, to avoid repeating a long name several times
P: emoml:pad-dimensions_pleasure
A: emoml:pad-dimensions_arousal
D: emoml:pad-dimensions_dominance
A: emoml:pad-dimensions:arousal
V: emoml:pad-dimensions:pleasure
D: emoml:pad-dimensions:dominance
anger: emoml:big6anger
disgust: emoml:big6disgust
fear: emoml:big6fear

View File

@@ -1,60 +0,0 @@
# Plugin emotion-anew
This plugin consists on an **emotion classifier** that detects six possible emotions:
- Anger : general-dislike.
- Fear : negative-fear.
- Disgust : shame.
- Joy : gratitude, affective, enthusiasm, love, joy, liking.
- Sadness : ingrattitude, daze, humlity, compassion, despair, anxiety, sadness.
- Neutral: not detected a particulary emotion.
The plugin uses **ANEW lexicon** dictionary to calculate VAD (valence-arousal-dominance) of the sentence and determinate which emotion is closer to this value. To do this comparision, it is defined that each emotion has a centroid, calculated according to this article: http://www.aclweb.org/anthology/W10-0208.
The plugin is going to look for the words in the sentence that appear in the ANEW dictionary and calculate the average VAD score for the sentence. Once this score is calculated, it is going to seek the emotion that is closest to this value.
The response of this plugin uses [Onyx ontology](https://www.gsi.dit.upm.es/ontologies/onyx/) developed at GSI UPM, to express the information.
## Installation
* Download
```
git clone https://lab.cluster.gsi.dit.upm.es/senpy/emotion-anew.git
```
* Get data
```
cd emotion-anew
git submodule update --init --recursive
```
* Run
```
docker run -p 5000:5000 -v $PWD:/plugins gsiupm/senpy:python2.7 -f /plugins
```
## Data format
`data/Corpus/affective-isear.tsv` contains data from ISEAR Databank: http://emotion-research.net/toolbox/toolboxdatabase.2006-10-13.2581092615
##Usage
Params accepted:
- Language: English (en) and Spanish (es).
- Input: input text to analyse.
Example request:
```
http://senpy.cluster.gsi.dit.upm.es/api/?algo=emotion-anew&language=en&input=I%20love%20Madrid
```
Example respond: This plugin follows the standard for the senpy plugin response. For more information, please visit [senpy documentation](http://senpy.readthedocs.io). Specifically, NIF API section.
# Known issues
- To obtain Anew dictionary you can download from here: <https://github.com/hcorona/SMC2015/blob/master/resources/ANEW2010All.txt>
- This plugin only supports **Python2**
![alt GSI Logo][logoGSI]
[logoES]: https://www.gsi.dit.upm.es/ontologies/onyx/img/eurosentiment_logo.png "EuroSentiment logo"
[logoGSI]: http://www.gsi.dit.upm.es/images/stories/logos/gsi.png "GSI Logo"

View File

@@ -1,269 +0,0 @@
# -*- coding: utf-8 -*-
import re
import nltk
import csv
import sys
import os
import unicodedata
import string
import xml.etree.ElementTree as ET
import math
from sklearn.svm import LinearSVC
from sklearn.feature_extraction import DictVectorizer
from nltk import bigrams
from nltk import trigrams
from nltk.corpus import stopwords
from pattern.en import parse as parse_en
from pattern.es import parse as parse_es
from senpy.plugins import EmotionPlugin, SenpyPlugin
from senpy.models import Results, EmotionSet, Entry, Emotion
### BEGIN WORKAROUND FOR PATTERN
# See: https://github.com/clips/pattern/issues/308
import os.path
import pattern.text
from pattern.helpers import decode_string
from codecs import BOM_UTF8
BOM_UTF8 = BOM_UTF8.decode("utf-8")
decode_utf8 = decode_string
MODEL = "emoml:pad-dimensions_"
VALENCE = f"{MODEL}_valence"
AROUSAL = f"{MODEL}_arousal"
DOMINANCE = f"{MODEL}_dominance"
def _read(path, encoding="utf-8", comment=";;;"):
"""Returns an iterator over the lines in the file at the given path,
strippping comments and decoding each line to Unicode.
"""
if path:
if isinstance(path, str) and os.path.exists(path):
# From file path.
f = open(path, "r", encoding="utf-8")
elif isinstance(path, str):
# From string.
f = path.splitlines()
else:
# From file or buffer.
f = path
for i, line in enumerate(f):
line = line.strip(BOM_UTF8) if i == 0 and isinstance(line, str) else line
line = line.strip()
line = decode_utf8(line, encoding)
if not line or (comment and line.startswith(comment)):
continue
yield line
pattern.text._read = _read
## END WORKAROUND
class ANEW(EmotionPlugin):
description = "This plugin consists on an emotion classifier using ANEW lexicon dictionary. It averages the VAD (valence-arousal-dominance) value of each word in the text that is also in the ANEW dictionary. To obtain a categorical value (e.g., happy) use the emotion conversion API (e.g., `emotion-model=emoml:big6`)."
author = "@icorcuera"
version = "0.5.2"
name = "emotion-anew"
extra_params = {
"language": {
"description": "language of the input",
"aliases": ["language", "l"],
"required": True,
"options": ["es","en"],
"default": "en"
}
}
anew_path_es = "Dictionary/Redondo(2007).csv"
anew_path_en = "Dictionary/ANEW2010All.txt"
onyx__usesEmotionModel = MODEL
nltk_resources = ['stopwords']
def activate(self, *args, **kwargs):
self._stopwords = stopwords.words('english')
dictionary={}
dictionary['es'] = {}
with self.open(self.anew_path_es,'r') as tabfile:
reader = csv.reader(tabfile, delimiter='\t')
for row in reader:
dictionary['es'][row[2]]={}
dictionary['es'][row[2]]['V']=row[3]
dictionary['es'][row[2]]['A']=row[5]
dictionary['es'][row[2]]['D']=row[7]
dictionary['en'] = {}
with self.open(self.anew_path_en,'r') as tabfile:
reader = csv.reader(tabfile, delimiter='\t')
for row in reader:
dictionary['en'][row[0]]={}
dictionary['en'][row[0]]['V']=row[2]
dictionary['en'][row[0]]['A']=row[4]
dictionary['en'][row[0]]['D']=row[6]
self._dictionary = dictionary
def _my_preprocessor(self, text):
regHttp = re.compile('(http://)[a-zA-Z0-9]*.[a-zA-Z0-9/]*(.[a-zA-Z0-9]*)?')
regHttps = re.compile('(https://)[a-zA-Z0-9]*.[a-zA-Z0-9/]*(.[a-zA-Z0-9]*)?')
regAt = re.compile('@([a-zA-Z0-9]*[*_/&%#@$]*)*[a-zA-Z0-9]*')
text = re.sub(regHttp, '', text)
text = re.sub(regAt, '', text)
text = re.sub('RT : ', '', text)
text = re.sub(regHttps, '', text)
text = re.sub('[0-9]', '', text)
text = self._delete_punctuation(text)
return text
def _delete_punctuation(self, text):
exclude = set(string.punctuation)
s = ''.join(ch for ch in text if ch not in exclude)
return s
def _extract_ngrams(self, text, lang):
unigrams_lemmas = []
unigrams_words = []
pos_tagged = []
if lang == 'es':
sentences = list(parse_es(text, lemmata=True).split())
else:
sentences = list(parse_en(text, lemmata=True).split())
for sentence in sentences:
for token in sentence:
if token[0].lower() not in self._stopwords:
unigrams_words.append(token[0].lower())
unigrams_lemmas.append(token[4])
pos_tagged.append(token[1])
return unigrams_lemmas,unigrams_words,pos_tagged
def _find_ngrams(self, input_list, n):
return zip(*[input_list[i:] for i in range(n)])
def _extract_features(self, tweet,dictionary,lang):
feature_set={}
ngrams_lemmas,ngrams_words,pos_tagged = self._extract_ngrams(tweet,lang)
pos_tags={'NN':'NN', 'NNS':'NN', 'JJ':'JJ', 'JJR':'JJ', 'JJS':'JJ', 'RB':'RB', 'RBR':'RB',
'RBS':'RB', 'VB':'VB', 'VBD':'VB', 'VGB':'VB', 'VBN':'VB', 'VBP':'VB', 'VBZ':'VB'}
totalVAD=[0,0,0]
matches=0
for word in range(len(ngrams_lemmas)):
VAD=[]
if ngrams_lemmas[word] in dictionary:
matches+=1
totalVAD = [totalVAD[0]+float(dictionary[ngrams_lemmas[word]]['V']),
totalVAD[1]+float(dictionary[ngrams_lemmas[word]]['A']),
totalVAD[2]+float(dictionary[ngrams_lemmas[word]]['D'])]
elif ngrams_words[word] in dictionary:
matches+=1
totalVAD = [totalVAD[0]+float(dictionary[ngrams_words[word]]['V']),
totalVAD[1]+float(dictionary[ngrams_words[word]]['A']),
totalVAD[2]+float(dictionary[ngrams_words[word]]['D'])]
if matches==0:
emotion='neutral'
else:
totalVAD=[totalVAD[0]/matches,totalVAD[1]/matches,totalVAD[2]/matches]
feature_set['V'] = totalVAD[0]
feature_set['A'] = totalVAD[1]
feature_set['D'] = totalVAD[2]
return feature_set
def analyse_entry(self, entry, activity):
params = activity.params
text_input = entry.text
text = self._my_preprocessor(text_input)
dictionary = self._dictionary[params['language']]
feature_set=self._extract_features(text, dictionary, params['language'])
emotions = EmotionSet()
emotions.id = "Emotions0"
emotion1 = Emotion(id="Emotion0")
emotion1[VALENCE] = feature_set['V']
emotion1[AROUSAL] = feature_set['A']
emotion1[DOMINANCE] = feature_set['D']
emotion1.prov(activity)
emotions.prov(activity)
emotions.onyx__hasEmotion.append(emotion1)
entry.emotions = [emotions, ]
yield entry
test_cases = [
{
'name': 'anger with VAD=(2.12, 6.95, 5.05)',
'input': 'I hate you',
'expected': {
'onyx:hasEmotionSet': [{
'onyx:hasEmotion': [{
AROUSAL: 6.95,
DOMINANCE: 5.05,
VALENCE: 2.12,
}]
}]
}
}, {
'input': 'i am sad',
'expected': {
'onyx:hasEmotionSet': [{
'onyx:hasEmotion': [{
f"{MODEL}_arousal": 4.13,
}]
}]
}
}, {
'name': 'joy',
'input': 'i am happy with my marks',
'expected': {
'onyx:hasEmotionSet': [{
'onyx:hasEmotion': [{
AROUSAL: 6.49,
DOMINANCE: 6.63,
VALENCE: 8.21,
}]
}]
}
}, {
'name': 'negative-feat',
'input': 'This movie is scary',
'expected': {
'onyx:hasEmotionSet': [{
'onyx:hasEmotion': [{
AROUSAL: 5.8100000000000005,
DOMINANCE: 4.33,
VALENCE: 5.050000000000001,
}]
}]
}
}, {
'name': 'negative-fear',
'input': 'this cake is disgusting' ,
'expected': {
'onyx:hasEmotionSet': [{
'onyx:hasEmotion': [{
AROUSAL: 5.09,
DOMINANCE: 4.4,
VALENCE: 5.109999999999999,
}]
}]
}
}
]

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@@ -1,12 +0,0 @@
---
module: emotion-anew
optional: true
requirements:
- numpy
- pandas
- nltk
- scipy
- scikit-learn
- textblob
- pattern
- lxml

View File

@@ -1,179 +0,0 @@
#!/usr/local/bin/python
# coding: utf-8
from future import standard_library
standard_library.install_aliases()
import os
import re
import sys
import string
import numpy as np
from six.moves import urllib
from nltk.corpus import stopwords
from senpy import EmotionBox, models
def ignore(dchars):
deletechars = "".join(dchars)
tbl = str.maketrans("", "", deletechars)
ignore = lambda s: s.translate(tbl)
return ignore
class DepecheMood(EmotionBox):
'''
Plugin that uses the DepecheMood emotion lexicon.
DepecheMood is an emotion lexicon automatically generated from news articles where users expressed their associated emotions. It contains two languages (English and Italian), as well as three types of word representations (token, lemma and lemma#PoS). For English, the lexicon contains 165k tokens, while the Italian version contains 116k. Unsupervised techniques can be applied to generate simple but effective baselines. To learn more, please visit https://github.com/marcoguerini/DepecheMood and http://www.depechemood.eu/
'''
author = 'Oscar Araque'
name = 'emotion-depechemood'
version = '0.1'
requirements = ['pandas']
optional = True
nltk_resources = ["stopwords"]
onyx__usesEmotionModel = 'wna:WNAModel'
EMOTIONS = ['wna:negative-fear',
'wna:amusement',
'wna:anger',
'wna:annoyance',
'wna:indifference',
'wna:joy',
'wna:awe',
'wna:sadness']
DM_EMOTIONS = ['AFRAID', 'AMUSED', 'ANGRY', 'ANNOYED', 'DONT_CARE', 'HAPPY', 'INSPIRED', 'SAD',]
def __init__(self, *args, **kwargs):
super(DepecheMood, self).__init__(*args, **kwargs)
self.LEXICON_URL = "https://github.com/marcoguerini/DepecheMood/raw/master/DepecheMood%2B%2B/DepecheMood_english_token_full.tsv"
self._denoise = ignore(set(string.punctuation)|set('«»'))
self._stop_words = []
self._lex_vocab = None
self._lex = None
def activate(self):
self._lex = self.download_lex()
self._lex_vocab = set(list(self._lex.keys()))
self._stop_words = stopwords.words('english') + ['']
def clean_str(self, string):
string = re.sub(r"[^A-Za-z0-9().,!?\'\`]", " ", string)
string = re.sub(r"[0-9]+", " num ", string)
string = re.sub(r"\'s", " \'s", string)
string = re.sub(r"\'ve", " \'ve", string)
string = re.sub(r"n\'t", " n\'t", string)
string = re.sub(r"\'re", " \'re", string)
string = re.sub(r"\'d", " \'d", string)
string = re.sub(r"\'ll", " \'ll", string)
string = re.sub(r"\.", " . ", string)
string = re.sub(r",", " , ", string)
string = re.sub(r"!", " ! ", string)
string = re.sub(r"\(", " ( ", string)
string = re.sub(r"\)", " ) ", string)
string = re.sub(r"\?", " ? ", string)
string = re.sub(r"\s{2,}", " ", string)
return string.strip().lower()
def preprocess(self, text):
if text is None:
return None
tokens = self._denoise(self.clean_str(text)).split(' ')
tokens = [tok for tok in tokens if tok not in self._stop_words]
return tokens
def estimate_emotion(self, tokens, emotion):
s = []
for tok in tokens:
s.append(self._lex[tok][emotion])
dividend = np.sum(s) if np.sum(s) > 0 else 0
divisor = len(s) if len(s) > 0 else 1
S = np.sum(s) / divisor
return S
def estimate_all_emotions(self, tokens):
S = []
intersection = set(tokens) & self._lex_vocab
for emotion in self.DM_EMOTIONS:
s = self.estimate_emotion(intersection, emotion)
S.append(s)
return S
def download_lex(self, file_path='DepecheMood_english_token_full.tsv', freq_threshold=10):
import pandas as pd
try:
file_path = self.find_file(file_path)
except IOError:
file_path = self.path(file_path)
filename, _ = urllib.request.urlretrieve(self.LEXICON_URL, file_path)
lexicon = pd.read_csv(file_path, sep='\t', index_col=0)
lexicon = lexicon[lexicon['freq'] >= freq_threshold]
lexicon.drop('freq', axis=1, inplace=True)
lexicon = lexicon.T.to_dict()
return lexicon
def predict_one(self, features, **kwargs):
tokens = self.preprocess(features[0])
estimation = self.estimate_all_emotions(tokens)
return estimation
test_cases = [
{
'entry': {
'nif:isString': 'My cat is very happy',
},
'expected': {
'onyx:hasEmotionSet': [
{
'onyx:hasEmotion': [
{
'onyx:hasEmotionCategory': 'wna:negative-fear',
'onyx:hasEmotionIntensity': 0.05278117640010922
},
{
'onyx:hasEmotionCategory': 'wna:amusement',
'onyx:hasEmotionIntensity': 0.2114806151413433,
},
{
'onyx:hasEmotionCategory': 'wna:anger',
'onyx:hasEmotionIntensity': 0.05726119426520887
},
{
'onyx:hasEmotionCategory': 'wna:annoyance',
'onyx:hasEmotionIntensity': 0.12295990731053638,
},
{
'onyx:hasEmotionCategory': 'wna:indifference',
'onyx:hasEmotionIntensity': 0.1860159893608025,
},
{
'onyx:hasEmotionCategory': 'wna:joy',
'onyx:hasEmotionIntensity': 0.12904050973724163,
},
{
'onyx:hasEmotionCategory': 'wna:awe',
'onyx:hasEmotionIntensity': 0.17973650399862967,
},
{
'onyx:hasEmotionCategory': 'wna:sadness',
'onyx:hasEmotionIntensity': 0.060724103786128455,
},
]
}
]
}
}
]
if __name__ == '__main__':
from senpy.utils import easy_test
easy_test(debug=False)

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@@ -0,0 +1,187 @@
from senpy import AnalysisPlugin, easy
class maxSentiment(AnalysisPlugin):
'''Plugin to extract max emotion from a multi-empotion set'''
author = '@dsuarezsouto'
version = '0.1'
extra_params = {
'max': {
"aliases": ["maximum", "max"],
'required': True,
'options': [True, False],
"@id": 'maxSentiment',
'default': False
}
}
def analyse_entry(self, entry, params):
if not params["max"]:
yield entry
return
set_emotions= entry.emotions[0]['onyx:hasEmotion']
max_emotion =set_emotions[0]
# Extract max emotion from the set emotions (emotion with highest intensity)
for tmp_emotion in set_emotions:
if tmp_emotion['onyx:hasEmotionIntensity']>max_emotion['onyx:hasEmotionIntensity']:
max_emotion=tmp_emotion
if max_emotion['onyx:hasEmotionIntensity'] == 0:
max_emotion['onyx:hasEmotionCategory'] = "neutral"
max_emotion['onyx:hasEmotionIntensity'] = 1.0
entry.emotions[0]['onyx:hasEmotion'] = [max_emotion]
entry.emotions[0]['prov:wasGeneratedBy'] = "maxSentiment"
#print(entry)
yield entry
# Test Cases:
# 1 Normal Situation.
# 2 Case to return a Neutral Emotion.
test_cases = [{
"entry": {
"@type": "entry",
"emotions": [
{
"@id": "Emotions0",
"@type": "emotionSet",
"onyx:hasEmotion": [
{
"@id": "_:Emotion_1538121033.74",
"@type": "emotion",
"onyx:hasEmotionCategory": "anger",
"onyx:hasEmotionIntensity": 0
},
{
"@id": "_:Emotion_1538121033.74",
"@type": "emotion",
"onyx:hasEmotionCategory": "joy",
"onyx:hasEmotionIntensity": 0.3333333333333333
},
{
"@id": "_:Emotion_1538121033.74",
"@type": "emotion",
"onyx:hasEmotionCategory": "negative-fear",
"onyx:hasEmotionIntensity": 0
},
{
"@id": "_:Emotion_1538121033.74",
"@type": "emotion",
"onyx:hasEmotionCategory": "sadness",
"onyx:hasEmotionIntensity": 0
},
{
"@id": "_:Emotion_1538121033.74",
"@type": "emotion",
"onyx:hasEmotionCategory": "disgust",
"onyx:hasEmotionIntensity": 0
}
]
},
],
"nif:isString": "This text makes me sad.\nwhilst this text makes me happy and surprised at the same time.\nI cannot believe it!"
},
'params': {
'max': True
},
'expected' : {
"@type": "entry",
"emotions": [
{
"@id": "Emotions0",
"@type": "emotionSet",
"onyx:hasEmotion": [
{
"@id": "_:Emotion_1538121033.74",
"@type": "emotion",
"onyx:hasEmotionCategory": "joy",
"onyx:hasEmotionIntensity": 0.3333333333333333
}
],
"prov:wasGeneratedBy" : 'maxSentiment'
}
],
"nif:isString": "This text makes me sad.\nwhilst this text makes me happy and surprised at the same time.\nI cannot believe it!"
}
},
{
"entry": {
"@type": "entry",
"emotions": [
{
"@id": "Emotions0",
"@type": "emotionSet",
"onyx:hasEmotion": [
{
"@id": "_:Emotion_1538121033.74",
"@type": "emotion",
"onyx:hasEmotionCategory": "anger",
"onyx:hasEmotionIntensity": 0
},
{
"@id": "_:Emotion_1538121033.74",
"@type": "emotion",
"onyx:hasEmotionCategory": "joy",
"onyx:hasEmotionIntensity": 0
},
{
"@id": "_:Emotion_1538121033.74",
"@type": "emotion",
"onyx:hasEmotionCategory": "negative-fear",
"onyx:hasEmotionIntensity": 0
},
{
"@id": "_:Emotion_1538121033.74",
"@type": "emotion",
"onyx:hasEmotionCategory": "sadness",
"onyx:hasEmotionIntensity": 0
},
{
"@id": "_:Emotion_1538121033.74",
"@type": "emotion",
"onyx:hasEmotionCategory": "disgust",
"onyx:hasEmotionIntensity": 0
}
]
}
],
"nif:isString": "This text makes me sad.\nwhilst this text makes me happy and surprised at the same time.\nI cannot believe it!"
},
'params': {
'max': True
},
'expected' : {
"@type": "entry",
"emotions": [
{
"@id": "Emotions0",
"@type": "emotionSet",
"onyx:hasEmotion": [
{
"@id": "_:Emotion_1538121033.74",
"@type": "emotion",
"onyx:hasEmotionCategory": "neutral",
"onyx:hasEmotionIntensity": 1
}
],
"prov:wasGeneratedBy" : 'maxSentiment'
}
],
"nif:isString": "This text makes me sad.\nwhilst this text makes me happy and surprised at the same time.\nI cannot believe it!"
}
}]
if __name__ == '__main__':
easy()

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@@ -1,3 +0,0 @@
FROM gsiupm/senpy:python{{PYVERSION}}
MAINTAINER manuel.garcia-amado.sancho@alumnos.upm.es

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