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