Push pip for tags without a preceding v

* Add git fetch to (try to) fix github push from gitlab

.gitlab-ci.yml

@@ -72,7 +72,7 @@ deploy_pypi:
     - make pip_upload
     - echo "" > ~/.pypirc && rm ~/.pypirc  # If the above fails, this won't run.
   only:
-    - /^v\d+\.\d+\.\d+([abc]\d*)?$/  # PEP-440 compliant version (tags)
+    - /^v?\d+\.\d+\.\d+([abc]\d*)?$/  # PEP-440 compliant version (tags)
   except:
     - branches
 
@@ -84,7 +84,15 @@ deploy:
   only:
     - master
 
-clean_docker :
+push-github:
+  stage: deploy
+  script:
+    - make -e push-github
+  only:
+    - master
+    - triggers
+
+clean :
   stage: clean
   script:
     - make -e clean

.travis.yml

@@ -7,7 +7,6 @@ language: python
 
 env:
   - PYV=2.7
-  - PYV=3.4
   - PYV=3.5
 # run nosetests - Tests
 script: make test-$PYV

Makefile

@@ -12,6 +12,7 @@ DEVPORT=5000
 
 TARNAME=$(NAME)-$(VERSION).tar.gz 
 action="test-${PYMAIN}"
+GITHUB_REPO=git@github.com:gsi-upm/senpy.git
 
 KUBE_CA_PEM_FILE=""
 KUBE_URL=""
@@ -132,6 +133,7 @@ push-github:
 	@echo "$$GITHUB_DEPLOY_KEY" > $(KEY_FILE)
 	@git remote rm github-deploy || true
 	git remote add github-deploy $(GITHUB_REPO)
+	@GIT_SSH_COMMAND="ssh -i $(KEY_FILE)" git fetch github-deploy $(CI_COMMIT_REF_NAME) || true
 	@GIT_SSH_COMMAND="ssh -i $(KEY_FILE)" git push github-deploy $(CI_COMMIT_REF_NAME)
 	rm $(KEY_FILE)
 

docs/SenpyClientUse.ipynb

@@ -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
+}

docs/about.rst

@@ -1,4 +1,4 @@
-Research
+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>`__):

docs/apischema.rst

@@ -1,7 +1,7 @@
-API and Schema
+API and Examples
-##############
+################
 .. toctree::
 
    vocabularies.rst
    api.rst
-   schema.rst
+   examples.rst

docs/architecture.rst

@@ -1,15 +0,0 @@
-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

docs/commandline.rst

@@ -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

docs/conf.py

@@ -37,7 +37,7 @@ extensions = [
     'sphinx.ext.todo',
     'sphinxcontrib.httpdomain',
     'sphinx.ext.coverage',
-    'sphinx.ext.autosectionlabel'
+    'sphinx.ext.autosectionlabel',
 ]
 
 # Add any paths that contain templates here, relative to this directory.

docs/examples.rst

@@ -1,4 +1,4 @@
-Schema
+Examples
 ------
 All the examples in this page use the :download:`the main schema <_static/schemas/definitions.json>`.
 
@@ -17,6 +17,8 @@ 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.
 
 Representation
 ,,,,,,,,,,,,,,
@@ -29,6 +31,7 @@ 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
 ,,,,,,,,,,,,,,
@@ -41,6 +44,7 @@ Emotion Analysis
 ................
 Description
 ,,,,,,,,,,,
+This annotation represents the emotion analysis of an input to Senpy. The emotions are contained in the emotions section with the text that refers to following Onyx format and the emotion model defined beforehand.
 
 Representation
 ,,,,,,,,,,,,,,
@@ -53,7 +57,7 @@ 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
 ,,,,,,,,,,,,,,
 

docs/index.rst

@@ -1,28 +1,35 @@
 Welcome to Senpy's documentation!
 =================================
-
+.. image:: https://readthedocs.org/projects/senpy/badge/?version=latest
-With Senpy, you can easily turn your sentiment or emotion analysis algorithm into a full blown semantic service.
+   :target: http://senpy.readthedocs.io/en/latest/
-Sharing your sentiment analysis with the world has never been easier.
+.. image:: https://badge.fury.io/py/senpy.svg
-Senpy provides:
+   :target: https://badge.fury.io/py/senpy
-
+.. image:: https://lab.cluster.gsi.dit.upm.es/senpy/senpy/badges/master/build.svg
-* Parameter validation, error handling
+   :target: https://lab.cluster.gsi.dit.upm.es/senpy/senpy/commits/master
-* Formatting: JSON-LD, Turtle/n-triples input and output, or simple text input
+.. image:: https://lab.cluster.gsi.dit.upm.es/senpy/senpy/badges/master/coverage.svg
-* Linked Data. Results are semantically annotated, using a series of well established vocabularies, and sane default URIs.
+   :target: https://lab.cluster.gsi.dit.upm.es/senpy/senpy/commits/master
-* A web UI where users can explore your service and test different settings
+.. image:: https://img.shields.io/pypi/l/requests.svg
-* A client to interact with any senpy service
+   :target: https://lab.cluster.gsi.dit.upm.es/senpy/senpy/
-* A command line tool
 
 
 
+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.
+
+.. image:: senpy-architecture.png
+  :width: 100%
+  :align: center
+
 .. toctree::
-   :caption: Learn more about senpy
+   :caption: Learn more about senpy:
    :maxdepth: 2
 
    senpy
    installation
+   demo
    usage
    apischema
    plugins
    conversion
-   demo
+   about
-   research.rst

docs/installation.rst

@@ -1,6 +1,16 @@
 Installation
 ------------
-The stable version can be installed in three ways.
+The stable version can be used in two ways: as a system/user library through pip, or as a docker image.
+
+The docker image is the recommended way because it is self-contained and isolated from the system, which means:
+
+  * 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
+
+Additionally, you may create your own docker image with your custom plugins, ready to be used by others.
+
 
 Through PIP
 ***********
@@ -33,15 +43,6 @@ 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 --default-plugins -f /plugins
-
-Alias
-.....
-
-If you are using the docker approach regularly, it is advisable to use a script or an alias to simplify your executions:
-
-.. code:: bash
-
-   alias senpy='docker run --rm -ti -p 5000:5000 -v $PWD:/senpy-plugins gsiupm/senpy --default-plugins'
  
 
 Python 2
@@ -54,3 +55,18 @@ There is a Senpy version for python2 too:
    docker run -ti -p 5000:5000 gsiupm/senpy:python2.7 --host 0.0.0.0 --default-plugins
 
 
+Alias
+.....
+
+If you are using the docker approach regularly, it is advisable to use a script or an alias to simplify your executions:
+
+.. code:: bash
+
+   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:
+
+.. code:: bash
+
+   senpy --version

docs/plugins.rst

@@ -4,6 +4,8 @@ This document describes how to develop a new analysis plugin. For an example of
 
 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?
 =================
 
@@ -113,7 +115,7 @@ The definition file would look like this:
           module: helloworld
           version: 0.0
           threshold: 10
-
+          description: Hello World
 
 Now, in a file named ``helloworld.py``:
 
@@ -122,11 +124,11 @@ Now, in a file named ``helloworld.py``:
           #!/bin/env python
           #helloworld.py
 
-          from senpy.plugins import SenpyPlugin
+          from senpy.plugins import AnalysisPlugin
           from senpy.models import Sentiment
 
 
-          class HelloWorld(SenpyPlugin):
+          class HelloWorld(AnalysisPlugin):
 
               def analyse_entry(entry, params):
                   '''Basically do nothing with each entry'''
@@ -139,6 +141,96 @@ Now, in a file named ``helloworld.py``:
                   entry.sentiments.append(sentiment)
                   yield entry
 
+The complete code of the example plugin is available `here <https://lab.cluster.gsi.dit.upm.es/senpy/plugin-prueba>`__.
+
+Loading data and files
+======================
+
+Most plugins will need access to files (dictionaries, lexicons, etc.).
+It is good practice to specify the paths of these files in the plugin configuration, so the same code can be reused with different resources.
+
+
+.. code:: yaml
+
+          name: dictworld
+          module: dictworld
+          dictionary_path: <PATH OF THE FILE>
+         
+The path can be either absolute, or relative.
+
+From absolute paths
+???????????????????
+
+Absolute paths (such as ``/data/dictionary.csv`` are straightfoward:
+
+.. code:: python
+
+   with open(os.path.join(self.dictionary_path) as f:
+      ...
+
+From relative paths
+???????????????????
+Since plugins are loading dynamically, relative paths will refer to the current working directory.
+Instead, what you usually want is to load files *relative to the plugin source folder*, like so:
+
+
+:: 
+
+   .
+   ..
+   plugin.senpy
+   plugin.py
+   dictionary.csv
+
+For this, we need to first get the path of your source folder first, like so:
+
+.. code:: python
+
+   import os
+   root = os.path.realpath(__file__)
+   with open(os.path.join(root, self.dictionary_path) as f:
+       ...
+
+
+Docker image
+============
+
+Add the following dockerfile to your project to generate a docker image with your plugin:
+
+.. code:: dockerfile
+
+   FROM gsiupm/senpy:0.8.8
+
+This will copy your source folder to the image, and install all dependencies.
+Now, to build an image:
+
+.. code:: shell
+
+   docker build . -t gsiupm/exampleplugin
+
+And you can run it with:
+
+.. code:: shell
+
+   docker run -p 5000:5000 gsiupm/exampleplugin
+
+
+If the plugin non-source files (:ref:`loading data and files`), the recommended way is to use absolute paths.
+Data can then be mounted in the container or added to the image.
+The former is recommended for open source plugins with licensed resources, whereas the latter is the most convenient and can be used for private images.
+
+Mounting data:
+
+.. code:: bash
+
+   docker run -v $PWD/data:/data gsiupm/exampleplugin
+
+Adding data to the image:
+
+.. code:: dockerfile
+
+   FROM gsiupm/senpy:0.8.8
+   COPY data /
 
 F.A.Q.
 ======
@@ -146,7 +238,7 @@ What annotations can I use?
 ???????????????????????????
 
 You can add almost any annotation to an entry.
-The most common use cases are covered in the :doc:`schema`.
+The most common use cases are covered in the :doc:`apischema`.
 
 
 Why does the analyse function yield instead of return?
@@ -154,7 +246,7 @@ Why does the analyse function yield instead of return?
 
 This is so that plugins may add new entries to the response or filter some of them.
 For instance, a `context detection` plugin may add a new entry for each context in the original entry.
-On the other hand, a conveersion plugin may leave out those entries that do not contain relevant information.
+On the other hand, a conversion plugin may leave out those entries that do not contain relevant information.
 
 
 If I'm using a classifier, where should I train it?
@@ -164,9 +256,9 @@ Training a classifier can be time time consuming. To avoid running the training
 
 .. code:: python
 
-          from senpy.plugins import ShelfMixin, SenpyPlugin
+          from senpy.plugins import ShelfMixin, AnalysisPlugin
 
-          class MyPlugin(ShelfMixin, SenpyPlugin):
+          class MyPlugin(ShelfMixin, AnalysisPlugin):
               def train(self):
                   ''' Code to train the classifier
                   '''
@@ -189,10 +281,10 @@ Shelves may get corrupted if the plugin exists unexpectedly.
 A corrupt shelf prevents the plugin from loading.
 If you do not care about the pickle, you can force your plugin to remove the corrupted file and load anyway, set the  'force_shelf' to True in your .senpy file.
 
-I want to implement my service as a plugin, How i can do it?
+How can I turn an external service into a plugin?
-????????????????????????????????????????????????????????????
+?????????????????????????????????????????????????
 
-This example ilustrate how to implement the Sentiment140 service as a plugin in senpy
+This example ilustrate how to implement a plugin that accesses the Sentiment140 service.
 
 .. code:: python
 
@@ -226,26 +318,30 @@ This example ilustrate how to implement the Sentiment140 service as a plugin in
                   yield entry
 
 
-Where can I define extra parameters to be introduced in the request to my plugin?
+Can my plugin require additional parameters from the user?
-?????????????????????????????????????????????????????????????????????????????????
+??????????????????????????????????????????????????????????
 
-You can add these parameters in the definition file under the attribute "extra_params" : "{param_name}". The name of the parameter has new attributes-value pairs. The basic attributes are:
+You can add extra parameters in the definition file under the attribute ``extra_params``.
+It takes a dictionary, where the keys are the name of the argument/parameter, and the value has the following fields:
 
 * aliases: the different names which can be used in the request to use the parameter.
-* required: this option is a boolean and indicates if the parameters is binding in operation plugin.
+* required: if set to true, users need to provide this parameter unless a default is set.
-* options: the different values of the paremeter.
+* options: the different acceptable values of the parameter (i.e. an enum). If set, the value provided must match one of the options.
-* default: the default value of the parameter, this is useful in case the paremeter is required and you want to have a default value.
+* default: the default value of the parameter, if none is provided in the request.
 
 .. code:: python
 
-          "extra_params": {
+          extra_params
-             "language": {
+             language:
-                "aliases": ["language", "l"],
+                aliases:
-                "required": true,
+                    - language
-                "options": ["es","en"],
+                    - lang
-                "default": "es"
+                    - l
-             }
+                required: true,
-          }
+                options:
+                    - es
+                    - en
+                default: es
 
 This example shows how to introduce a parameter associated with language.
 The extraction of this paremeter is used in the analyse method of the Plugin interface.
@@ -277,7 +373,6 @@ Additionally, with the ``--pdb`` option you will be dropped into a pdb post mort
 
    senpy --pdb
 
-
 Where can I find more code examples?
 ????????????????????????????????????
 

docs/requirements.txt

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

docs/senpy.rst

@@ -1,6 +1,26 @@
 What is Senpy?
 --------------
 
+Web services can get really complex: data validation, user interaction, formatting, logging., etc. 
+The figure below summarizes the typical features in an analysis service.
+Senpy implements all the common blocks, so developers can focus on what really matters: great analysis algorithms that solve real problems.
+
+.. image:: senpy-framework.png
+  :width: 60%
+  :align: center
+
+
+Senpy for end users
+===================
+
+All services built using senpy share a common interface.
+This allows users to use them (almost) interchangeably.
+Senpy comes with a :ref:`built-in client`.
+
+
+Senpy for service developers
+============================
+
 Senpy is a framework that turns your sentiment or emotion analysis algorithm into a full blown semantic service.
 Senpy takes care of:
 
@@ -12,21 +32,20 @@ Senpy takes care of:
 
 Sharing your sentiment analysis with the world has never been easier!
 
-Senpy for service developers
-============================
-
 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.
 
-Senpy for end users
+Architecture
-===================
+============
 
-All services built using senpy share a common interface.
+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.
-This allows users to use them (almost) interchangeably.
-Senpy comes with a :ref:`built-in client`.
 
+Senpy proposes a modular and dynamic architecture that allows:
 
-.. toctree::
+* Implementing different algorithms in a extensible way, yet offering a common interface.
-    :caption: Interested? Check out senpy's:
+* Offering common services that facilitate development, so developers can focus on implementing new and better algorithms.
- 
-    architecture
 
+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

docs/server.rst

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

docs/usage.rst

@@ -2,72 +2,14 @@ Usage
 -----
 
 First of all, you need to install the package.
-See :doc:`installation` for installation instructions.
+See :doc:`installation` for instructions.
 Once installed, the `senpy` command should be available. 
 
-Useful command-line options
+.. toctree::
-===========================
+   :maxdepth: 1
 
-In case you want to load modules, which are located in different folders under the root folder, use the next option.
+   server
-
+   SenpyClientUse
-.. code:: bash
+   commandline
-
-    senpy -f .
-
-The default port used by senpy is 5000, but you can change it using the `--port` flag.
-
-.. code:: bash
-
-    senpy --port 8080
-
-Also, the host can be changed where senpy is deployed. The default value is `127.0.0.1`.
-
-.. code:: bash
-
-    senpy --host 0.0.0.0
-
-For more options, see the `--help` page.
-
-Alternatively, you can use the modules included in senpy to build your own application.
-
-Senpy server
-============
-
-Once the server is launched, there is a basic endpoint in the server, which provides a playground to use the plugins that have been loaded.
-
-In case you want to know the different endpoints of the server, there is more information available in the NIF API section_.
-
-CLI demo
-========
-
-This video shows how to use senpy through command-line tool.
-
-.. image:: https://asciinema.org/a/9uwef1ghkjk062cw2t4mhzpyk.png
-   :width: 100%
-   :target: https://asciinema.org/a/9uwef1ghkjk062cw2t4mhzpyk
-   :alt: CLI demo
 
 
-Built-in client
-===============
-
-This example shows how to make a request to the default plugin:
-
-.. code:: python
-
-    from senpy.client import Client
-
-    c = Client('http://127.0.0.1:5000/api/')
-    r = c.analyse('hello world')
-
-    for entry in r.entries:
-          print('{} -> {}'.format(entry.text, entry.emotions))
-
-
-
-.. _section: http://senpy.readthedocs.org/en/latest/api.html
-
-
-Conversion
-==========
-See :doc:`conversion`

k8s/senpy-deployment.yaml

@@ -14,6 +14,7 @@ spec:
       containers:
       - name: senpy-latest
         image: gsiupm/senpy:latest
+        imagePullPolicy: Always
         args:
           - "--default-plugins"
         resources:

senpy/__main__.py

@@ -22,10 +22,6 @@ the server.
 
 from flask import Flask
 from senpy.extensions import Senpy
-from tornado.wsgi import WSGIContainer
-from tornado.httpserver import HTTPServer
-from tornado.ioloop import IOLoop
-
 
 import logging
 import os
@@ -78,6 +74,11 @@ def main():
         action='store_true',
         default=False,
         help='Do not run a server, only install plugin dependencies')
+    parser.add_argument(
+        '--threaded',
+        action='store_false',
+        default=True,
+        help='Run a threaded server')
     parser.add_argument(
         '--version',
         '-v',
@@ -101,18 +102,10 @@ def main():
     print('Senpy version {}'.format(senpy.__version__))
     print('Server running on port %s:%d. Ctrl+C to quit' % (args.host,
                                                             args.port))
-    if not app.debug:
+    app.run(args.host,
-        http_server = HTTPServer(WSGIContainer(app))
+            args.port,
-        http_server.listen(args.port, address=args.host)
+            threaded=args.threaded,
-        try:
+            debug=app.debug)
-            IOLoop.instance().start()
-        except KeyboardInterrupt:
-            print('Bye!')
-        http_server.stop()
-    else:
-        app.run(args.host,
-                args.port,
-                debug=True)
     sp.deactivate_all()
 
 

senpy/client.py

@@ -22,7 +22,6 @@ class Client(object):
         response = requests.request(method=method, url=url, params=params)
         try:
             resp = models.from_dict(response.json())
-            resp.validate(resp)
         except Exception as ex:
             logger.error(('There seems to be a problem with the response:\n'
                           '\tURL: {url}\n'

senpy/plugins/conversion/emotion/centroids.py

@@ -32,36 +32,58 @@ class CentroidConversion(EmotionConversionPlugin):
                     nv1[aliases.get(k2, k2)] = v2
                 ncentroids[aliases.get(k1, k1)] = nv1
             info['centroids'] = ncentroids
+
         super(CentroidConversion, self).__init__(info)
 
+        self.dimensions = set()
+        for c in self.centroids.values():
+            self.dimensions.update(c.keys())
+        self.neutralPoints = self.get("neutralPoints", dict())
+        if not self.neutralPoints:
+            for i in self.dimensions:
+                self.neutralPoints[i] = self.get("neutralValue", 0)
+
     def _forward_conversion(self, original):
-        """Sum the VAD value of all categories found."""
+        """Sum the VAD value of all categories found weighted by intensity.
+        Intensities are scaled by onyx:maxIntensityValue if it is present, else maxIntensityValue
+        is assumed to be one. Emotion entries that do not have onxy:hasEmotionIntensity specified
+        are assumed to have maxIntensityValue. Emotion entries that do not have
+        onyx:hasEmotionCategory specified are ignored."""
         res = Emotion()
+        maxIntensity = float(original.get("onyx:maxIntensityValue", 1))
         for e in original.onyx__hasEmotion:
-            category = e.onyx__hasEmotionCategory
+            category = e.get("onyx:hasEmotionCategory", None)
-            if category in self.centroids:
+            if not category:
-                for dim, value in self.centroids[category].items():
+                continue
-                    try:
+            intensity = e.get("onyx:hasEmotionIntensity", maxIntensity) / maxIntensity
-                        res[dim] += value
+            if not intensity:
-                    except Exception:
+                continue
-                        res[dim] = value
+            centroid = self.centroids.get(category, None)
+            if centroid:
+                for dim, value in centroid.items():
+                    neutral = self.neutralPoints[dim]
+                    if dim not in res:
+                        res[dim] = 0
+                    res[dim] += (value - neutral) * intensity + neutral
         return res
 
     def _backwards_conversion(self, original):
         """Find the closest category"""
-        dimensions = list(self.centroids.values())[0]
+        centroids = self.centroids
+        neutralPoints = self.neutralPoints
+        dimensions = self.dimensions
 
-        def distance(e1, e2):
+        def distance_k(centroid, original, k):
-            return sum((e1[k] - e2.get(k, 0)) for k in dimensions)
+            # k component of the distance between the value and a given centroid
+            return (centroid.get(k, neutralPoints[k]) - original.get(k, neutralPoints[k]))**2
+
+        def distance(centroid):
+            return sum(distance_k(centroid, original, k) for k in dimensions)
+
+        emotion = min(centroids, key=lambda x: distance(centroids[x]))
 
-        emotion = ''
-        mindistance = 10000000000000000000000.0
-        for state in self.centroids:
-            d = distance(self.centroids[state], original)
-            if d < mindistance:
-                mindistance = d
-                emotion = state
         result = Emotion(onyx__hasEmotionCategory=emotion)
+        result.onyx__algorithmConfidence = distance(centroids[emotion])
         return result
 
     def convert(self, emotionSet, fromModel, toModel, params):

senpy/plugins/conversion/emotion/ekman2fsre.senpy

@@ -1,6 +1,6 @@
 ---
 name: Ekman2FSRE
-module: senpy.plugins.conversion.centroids
+module: senpy.plugins.conversion.emotion.centroids
 description: Plugin to convert emotion sets from Ekman to VAD
 version: 0.1
 # No need to specify onyx:doesConversion because centroids.py adds it automatically from centroids_direction

senpy/plugins/conversion/emotion/ekman2vad.senpy

@@ -1,9 +1,14 @@
 ---
 name: Ekman2PAD
-module: senpy.plugins.conversion.centroids
+module: senpy.plugins.conversion.emotion.centroids
 description: Plugin to convert emotion sets from Ekman to VAD
 version: 0.1
 # No need to specify onyx:doesConversion because centroids.py adds it automatically from centroids_direction
+origin:
+  # Point in VAD space with no emotion (aka Neutral)
+  A: 5.0
+  D: 5.0
+  V: 5.0
 centroids:
   anger:
     A: 6.95
@@ -36,4 +41,4 @@ aliases: # These are aliases for any key in the centroid, to avoid repeating a l
   disgust: emoml:big6disgust
   fear: emoml:big6fear
   happiness: emoml:big6happiness
-  sadness: emoml:big6sadness
+  sadness: emoml:big6sadness

senpy/version.py

@@ -11,7 +11,7 @@ def read_version(versionfile=DEFAULT_FILE):
     try:
         with open(versionfile) as f:
             return f.read().strip()
-    except IOError:
+    except IOError:  # pragma: no cover
         logger.error('Running an unknown version of senpy. Be careful!.')
         return '0.0'
 

setup.cfg

@@ -11,4 +11,7 @@ max-line-length = 100
 [bdist_wheel]
 universal=1
 [tool:pytest]
-addopts = --cov=senpy --cov-report term-missing
+addopts = --cov=senpy --cov-report term-missing
+
+[coverage:report]
+omit = senpy/__main__.py

tests/test_plugins.py

@@ -6,8 +6,9 @@ import shutil
 import tempfile
 
 from unittest import TestCase
-from senpy.models import Results, Entry
+from senpy.models import Results, Entry, EmotionSet, Emotion
 from senpy.plugins import SentimentPlugin, ShelfMixin
+from senpy.plugins.conversion.emotion.centroids import CentroidConversion
 
 
 class ShelfDummyPlugin(SentimentPlugin, ShelfMixin):
@@ -152,3 +153,52 @@ class PluginsTest(TestCase):
             }
         })
         assert 'example' in a.extra_params
+
+    def test_conversion_centroids(self):
+        info = {
+            "name": "CentroidTest",
+            "description": "Centroid test",
+            "version": 0,
+            "centroids": {
+                "c1": {"V1": 0.5,
+                       "V2": 0.5},
+                "c2": {"V1": -0.5,
+                       "V2": 0.5},
+                "c3": {"V1": -0.5,
+                       "V2": -0.5},
+                "c4": {"V1": 0.5,
+                       "V2": -0.5}},
+            "aliases": {
+                "V1": "X-dimension",
+                "V2": "Y-dimension"
+            },
+            "centroids_direction": ["emoml:big6", "emoml:fsre-dimensions"]
+        }
+        c = CentroidConversion(info)
+
+        es1 = EmotionSet()
+        e1 = Emotion()
+        e1.onyx__hasEmotionCategory = "c1"
+        es1.onyx__hasEmotion.append(e1)
+        res = c._forward_conversion(es1)
+        assert res["X-dimension"] == 0.5
+        assert res["Y-dimension"] == 0.5
+
+        e2 = Emotion()
+        e2.onyx__hasEmotionCategory = "c2"
+        es1.onyx__hasEmotion.append(e2)
+        res = c._forward_conversion(es1)
+        assert res["X-dimension"] == 0
+        assert res["Y-dimension"] == 1
+
+        e = Emotion()
+        e["X-dimension"] = -0.2
+        e["Y-dimension"] = -0.3
+        res = c._backwards_conversion(e)
+        assert res["onyx:hasEmotionCategory"] == "c3"
+
+        e = Emotion()
+        e["X-dimension"] = -0.2
+        e["Y-dimension"] = 0.3
+        res = c._backwards_conversion(e)
+        assert res["onyx:hasEmotionCategory"] == "c2"