mirror of
https://github.com/gsi-upm/senpy
synced 2024-11-14 04:32:29 +00:00
380 lines
13 KiB
ReStructuredText
380 lines
13 KiB
ReStructuredText
Developing new plugins
|
|
----------------------
|
|
This document describes how to develop a new analysis plugin. For an example of conversion plugins, see :doc:`conversion`.
|
|
|
|
A more step-by-step tutorial with slides is available `here <https://lab.cluster.gsi.dit.upm.es/senpy/senpy-tutorial>`__
|
|
|
|
.. contents:: :local:
|
|
|
|
What is a plugin?
|
|
=================
|
|
|
|
A plugin is a program that, given a text, will add annotations to it.
|
|
In practice, a plugin consists of at least two files:
|
|
|
|
- Definition file: a `.senpy` file that describes the plugin (e.g. what input parameters it accepts, what emotion model it uses).
|
|
- Python module: the actual code that will add annotations to each input.
|
|
|
|
This separation allows us to deploy plugins that use the same code but employ different parameters.
|
|
For instance, one could use the same classifier and processing in several plugins, but train with different datasets.
|
|
This scenario is particularly useful for evaluation purposes.
|
|
|
|
The only limitation is that the name of each plugin needs to be unique.
|
|
|
|
Plugin Definition files
|
|
=======================
|
|
|
|
The definition file contains all the attributes of the plugin, and can be written in YAML or JSON.
|
|
When the server is launched, it will recursively search for definition files in the plugin folder (the current folder, by default).
|
|
The most important attributes are:
|
|
|
|
* **name**: unique name that senpy will use internally to identify the plugin.
|
|
* **module**: indicates the module that contains the plugin code, which will be automatically loaded by senpy.
|
|
* **version**
|
|
* extra_params: to add parameters to the senpy API when this plugin is requested. Those parameters may be required, and have aliased names. For instance:
|
|
|
|
.. code:: yaml
|
|
|
|
extra_params:
|
|
hello_param:
|
|
aliases: # required
|
|
- hello_param
|
|
- hello
|
|
required: true
|
|
default: Hi you
|
|
values:
|
|
- Hi you
|
|
- Hello y'all
|
|
- Howdy
|
|
|
|
Parameter validation will fail if a required parameter without a default has not been provided, or if the definition includes a set of values and the provided one does not match one of them.
|
|
|
|
|
|
A complete example:
|
|
|
|
.. code:: yaml
|
|
|
|
name: <Name of the plugin>
|
|
module: <Python file>
|
|
version: 0.1
|
|
|
|
And the json equivalent:
|
|
|
|
.. code:: json
|
|
|
|
{
|
|
"name": "<Name of the plugin>",
|
|
"module": "<Python file>",
|
|
"version": "0.1"
|
|
}
|
|
|
|
|
|
Plugins Code
|
|
============
|
|
|
|
The basic methods in a plugin are:
|
|
|
|
* __init__
|
|
* activate: used to load memory-hungry resources
|
|
* deactivate: used to free up resources
|
|
* analyse_entry: called in every user requests. It takes two parameters: ``Entry``, the entry object, and ``params``, the parameters supplied by the user. It should yield one or more ``Entry`` objects.
|
|
|
|
Plugins are loaded asynchronously, so don't worry if the activate method takes too long. The plugin will be marked as activated once it is finished executing the method.
|
|
|
|
Entries
|
|
=======
|
|
|
|
Entries are objects that can be annotated.
|
|
By default, entries are `NIF contexts <http://persistence.uni-leipzig.org/nlp2rdf/ontologies/nif-core/nif-core.html>`_ represented in JSON-LD format.
|
|
Annotations are added to the object like this:
|
|
|
|
.. code:: python
|
|
|
|
entry = Entry()
|
|
entry.vocabulary__annotationName = 'myvalue'
|
|
entry['vocabulary:annotationName'] = 'myvalue'
|
|
entry['annotationNameURI'] = 'myvalue'
|
|
|
|
Where vocabulary is one of the prefixes defined in the default senpy context, and annotationURI is a full URI.
|
|
The value may be any valid JSON-LD dictionary.
|
|
For simplicity, senpy includes a series of models by default in the ``senpy.models`` module.
|
|
|
|
|
|
Example plugin
|
|
==============
|
|
|
|
In this section, we will implement a basic sentiment analysis plugin.
|
|
To determine the polarity of each entry, the plugin will compare the length of the string to a threshold.
|
|
This threshold will be included in the definition file.
|
|
|
|
The definition file would look like this:
|
|
|
|
.. code:: yaml
|
|
|
|
name: helloworld
|
|
module: helloworld
|
|
version: 0.0
|
|
threshold: 10
|
|
description: Hello World
|
|
|
|
Now, in a file named ``helloworld.py``:
|
|
|
|
.. code:: python
|
|
|
|
#!/bin/env python
|
|
#helloworld.py
|
|
|
|
from senpy.plugins import AnalysisPlugin
|
|
from senpy.models import Sentiment
|
|
|
|
|
|
class HelloWorld(AnalysisPlugin):
|
|
|
|
def analyse_entry(entry, params):
|
|
'''Basically do nothing with each entry'''
|
|
|
|
sentiment = Sentiment()
|
|
if len(entry.text) < self.threshold:
|
|
sentiment['marl:hasPolarity'] = 'marl:Positive'
|
|
else:
|
|
sentiment['marl:hasPolarity'] = 'marl:Negative'
|
|
entry.sentiments.append(sentiment)
|
|
yield entry
|
|
|
|
The complete code of the example plugin is available `here <https://lab.cluster.gsi.dit.upm.es/senpy/plugin-prueba>`__.
|
|
|
|
Loading data and files
|
|
======================
|
|
|
|
Most plugins will need access to files (dictionaries, lexicons, etc.).
|
|
It is good practice to specify the paths of these files in the plugin configuration, so the same code can be reused with different resources.
|
|
|
|
|
|
.. code:: yaml
|
|
|
|
name: dictworld
|
|
module: dictworld
|
|
dictionary_path: <PATH OF THE FILE>
|
|
|
|
The path can be either absolute, or relative.
|
|
|
|
From absolute paths
|
|
???????????????????
|
|
|
|
Absolute paths (such as ``/data/dictionary.csv`` are straightfoward:
|
|
|
|
.. code:: python
|
|
|
|
with open(os.path.join(self.dictionary_path) as f:
|
|
...
|
|
|
|
From relative paths
|
|
???????????????????
|
|
Since plugins are loading dynamically, relative paths will refer to the current working directory.
|
|
Instead, what you usually want is to load files *relative to the plugin source folder*, like so:
|
|
|
|
|
|
::
|
|
|
|
.
|
|
..
|
|
plugin.senpy
|
|
plugin.py
|
|
dictionary.csv
|
|
|
|
For this, we need to first get the path of your source folder first, like so:
|
|
|
|
.. code:: python
|
|
|
|
import os
|
|
root = os.path.realpath(__file__)
|
|
with open(os.path.join(root, self.dictionary_path) as f:
|
|
...
|
|
|
|
|
|
Docker image
|
|
============
|
|
|
|
Add the following dockerfile to your project to generate a docker image with your plugin:
|
|
|
|
.. code:: dockerfile
|
|
|
|
FROM gsiupm/senpy:0.8.8
|
|
|
|
This will copy your source folder to the image, and install all dependencies.
|
|
Now, to build an image:
|
|
|
|
.. code:: shell
|
|
|
|
docker build . -t gsiupm/exampleplugin
|
|
|
|
And you can run it with:
|
|
|
|
.. code:: shell
|
|
|
|
docker run -p 5000:5000 gsiupm/exampleplugin
|
|
|
|
|
|
If the plugin non-source files (:ref:`loading data and files`), the recommended way is to use absolute paths.
|
|
Data can then be mounted in the container or added to the image.
|
|
The former is recommended for open source plugins with licensed resources, whereas the latter is the most convenient and can be used for private images.
|
|
|
|
Mounting data:
|
|
|
|
.. code:: bash
|
|
|
|
docker run -v $PWD/data:/data gsiupm/exampleplugin
|
|
|
|
Adding data to the image:
|
|
|
|
.. code:: dockerfile
|
|
|
|
FROM gsiupm/senpy:0.8.8
|
|
COPY data /
|
|
|
|
F.A.Q.
|
|
======
|
|
What annotations can I use?
|
|
???????????????????????????
|
|
|
|
You can add almost any annotation to an entry.
|
|
The most common use cases are covered in the :doc:`apischema`.
|
|
|
|
|
|
Why does the analyse function yield instead of return?
|
|
??????????????????????????????????????????????????????
|
|
|
|
This is so that plugins may add new entries to the response or filter some of them.
|
|
For instance, a `context detection` plugin may add a new entry for each context in the original entry.
|
|
On the other hand, a conversion plugin may leave out those entries that do not contain relevant information.
|
|
|
|
|
|
If I'm using a classifier, where should I train it?
|
|
???????????????????????????????????????????????????
|
|
|
|
Training a classifier can be time time consuming. To avoid running the training unnecessarily, you can use ShelfMixin to store the classifier. For instance:
|
|
|
|
.. code:: python
|
|
|
|
from senpy.plugins import ShelfMixin, AnalysisPlugin
|
|
|
|
class MyPlugin(ShelfMixin, AnalysisPlugin):
|
|
def train(self):
|
|
''' Code to train the classifier
|
|
'''
|
|
# Here goes the code
|
|
# ...
|
|
return classifier
|
|
|
|
def activate(self):
|
|
if 'classifier' not in self.sh:
|
|
classifier = self.train()
|
|
self.sh['classifier'] = classifier
|
|
self.classifier = self.sh['classifier']
|
|
|
|
def deactivate(self):
|
|
self.close()
|
|
|
|
You can specify a 'shelf_file' in your .senpy file. By default the ShelfMixin creates a file based on the plugin name and stores it in that plugin's folder.
|
|
|
|
Shelves may get corrupted if the plugin exists unexpectedly.
|
|
A corrupt shelf prevents the plugin from loading.
|
|
If you do not care about the pickle, you can force your plugin to remove the corrupted file and load anyway, set the 'force_shelf' to True in your .senpy file.
|
|
|
|
How can I turn an external service into a plugin?
|
|
?????????????????????????????????????????????????
|
|
|
|
This example ilustrate how to implement a plugin that accesses the Sentiment140 service.
|
|
|
|
.. code:: python
|
|
|
|
class Sentiment140Plugin(SentimentPlugin):
|
|
def analyse_entry(self, entry, params):
|
|
text = entry.text
|
|
lang = params.get("language", "auto")
|
|
res = requests.post("http://www.sentiment140.com/api/bulkClassifyJson",
|
|
json.dumps({"language": lang,
|
|
"data": [{"text": text}]
|
|
}
|
|
)
|
|
)
|
|
|
|
p = params.get("prefix", None)
|
|
polarity_value = self.maxPolarityValue*int(res.json()["data"][0]
|
|
["polarity"]) * 0.25
|
|
polarity = "marl:Neutral"
|
|
neutral_value = self.maxPolarityValue / 2.0
|
|
if polarity_value > neutral_value:
|
|
polarity = "marl:Positive"
|
|
elif polarity_value < neutral_value:
|
|
polarity = "marl:Negative"
|
|
|
|
sentiment = Sentiment(id="Sentiment0",
|
|
prefix=p,
|
|
marl__hasPolarity=polarity,
|
|
marl__polarityValue=polarity_value)
|
|
sentiment.prov__wasGeneratedBy = self.id
|
|
entry.sentiments.append(sentiment)
|
|
yield entry
|
|
|
|
|
|
Can my plugin require additional parameters from the user?
|
|
??????????????????????????????????????????????????????????
|
|
|
|
You can add extra parameters in the definition file under the attribute ``extra_params``.
|
|
It takes a dictionary, where the keys are the name of the argument/parameter, and the value has the following fields:
|
|
|
|
* aliases: the different names which can be used in the request to use the parameter.
|
|
* required: if set to true, users need to provide this parameter unless a default is set.
|
|
* options: the different acceptable values of the parameter (i.e. an enum). If set, the value provided must match one of the options.
|
|
* default: the default value of the parameter, if none is provided in the request.
|
|
|
|
.. code:: python
|
|
|
|
extra_params
|
|
language:
|
|
aliases:
|
|
- language
|
|
- lang
|
|
- l
|
|
required: true,
|
|
options:
|
|
- es
|
|
- en
|
|
default: es
|
|
|
|
This example shows how to introduce a parameter associated with language.
|
|
The extraction of this paremeter is used in the analyse method of the Plugin interface.
|
|
|
|
.. code:: python
|
|
|
|
lang = params.get("language")
|
|
|
|
Where can I set up variables for using them in my plugin?
|
|
?????????????????????????????????????????????????????????
|
|
|
|
You can add these variables in the definition file with the structure of attribute-value pairs.
|
|
|
|
Every field added to the definition file is available to the plugin instance.
|
|
|
|
Can I activate a DEBUG mode for my plugin?
|
|
???????????????????????????????????????????
|
|
|
|
You can activate the DEBUG mode by the command-line tool using the option -d.
|
|
|
|
.. code:: bash
|
|
|
|
senpy -d
|
|
|
|
|
|
Additionally, with the ``--pdb`` option you will be dropped into a pdb post mortem shell if an exception is raised.
|
|
|
|
.. code:: bash
|
|
|
|
senpy --pdb
|
|
|
|
Where can I find more code examples?
|
|
????????????????????????????????????
|
|
|
|
See: `<http://github.com/gsi-upm/senpy-plugins-community>`_.
|