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249 lines
8.8 KiB
ReStructuredText
249 lines
8.8 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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Each plugin represents a different analysis process.There are two types of files that are needed by senpy for loading a plugin:
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- Definition file, has the ".senpy" extension.
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- Code file, is a python file.
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This separation will allow 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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Plugins Definitions
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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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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: used to specify parameters that the plugin accepts that are not already part of the senpy API. 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 in the parameters supplied by a user and 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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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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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 SenpyPlugin
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from senpy.models import Sentiment
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class HelloWorld(SenpyPlugin):
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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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F.A.Q.
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======
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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 conveersion 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, SenpyPlugin
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class MyPlugin(ShelfMixin, SenpyPlugin):
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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 speficy 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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I want to implement my service as a plugin, How i can do it?
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????????????????????????????????????????????????????????????
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This example ilustrate how to implement the Sentiment140 service as a plugin in senpy
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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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Where can I define extra parameters to be introduced in the request to my plugin?
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?????????????????????????????????????????????????????????????????????????????????
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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:
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* aliases: the different names which can be used in the request to use the parameter.
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* required: this option is a boolean and indicates if the parameters is binding in operation plugin.
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* options: the different values of the paremeter.
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* default: the default value of the parameter, this is useful in case the paremeter is required and you want to have a default value.
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.. code:: python
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"extra_params": {
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"language": {
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"aliases": ["language", "l"],
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"required": true,
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"options": ["es","en"],
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"default": "es"
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}
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}
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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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