Each plugin represents a different analysis process.There are two types of files that are needed by senpy for loading a plugin:
Plugins Interface
=======
- Definition file, has the ".senpy" extension.
- Code file, is a python file.
This separation will allow 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.
Plugins Definitions
===================
The definition file can be written in JSON or YAML, where the data representation consists on attribute-value pairs.
The principal attributes are:
The definition file contains all the attributes of the plugin, and can be written in YAML or JSON.
The most important attributes are:
* name: plugin name used in senpy to call the plugin.
* module: indicates the module that will be loaded
* **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: 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:
..code:: python
..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" : "senpyPlugin",
"module" : "{python code file}"
"name": "<Name of the plugin>",
"module": "<Python file>",
"version": "0.1"
}
..code:: python
name: senpyPlugin
module: {python code file}
Plugins Code
=================
============
The basic methods in a plugin are:
* __init__
* activate: used to load memory-hungry resources
* deactivate: used to free up resources
* analyse: called in every user requests. It takes in the parameters supplied by a user and should return a senpy Response.
* analyse_entry: called in every user requests. It takes in the parameters supplied by a user and 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.
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
Now, in a file named ``helloworld.py``:
..code:: python
#!/bin/env python
#helloworld.py
from senpy.plugins import SenpyPlugin
from senpy.models import Sentiment
class HelloWorld(SenpyPlugin):
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
F.A.Q.
======
Why does the analyse function yield instead of return?
You can add these variables in the definition file with the extracture of attribute-value pair.
You can add these variables in the definition file with the structure of attribute-value pairs.
Once you have added your variables, the next step is to extract them into the plugin. The plugin's __init__ method has a parameter called `info` where you can extract the values of the variables. This info parameter has the structure of a python dictionary.
Every field added to the definition file is available to the plugin instance.
Can I activate a DEBUG mode for my plugin?
???????????????????????????????????????????
@ -154,7 +230,15 @@ You can activate the DEBUG mode by the command-line tool using the option -d.
..code:: bash
python -m senpy -d
senpy -d
Additionally, with the ``--pdb`` option you will be dropped into a pdb post mortem shell if an exception is raised.
<aclass="btn btn-lg btn-primary"href="#test"role="button">Test it »</a>
<divclass="col-lg-6">
<h2>About Senpy</h2>
<p>Senpy is a framework to build semantic sentiment and emotion analysis services. It does so by using a mix of web and semantic technologies, such as JSON-LD, RDFlib and Flask.</p>
<p>Senpy makes it easy to develop and publish your own analysis algorithms (plugins in senpy terms).
</p>
<p>
This website is the senpy Playground, which allows you to test the instance of senpy in this server. It provides a user-friendly interface to the functions exposed by the senpy API.
</p>
<p>
Once you get comfortable with the parameters and results, you are encouraged to issue your own requests to the API endpoint, which should be <ahref="/api">here</a>.
</p>
<p>
These are some of the things you can do with the API:
<ul>
<li>List all available plugins: <ahref="/api/plugins">/api/plugins</a></li>
<li>Get information about the default plugin: <ahref="/api/plugins/default">/api/plugins/default</a></li>
<li>Download the JSON-LD context used: <ahref="/api/contexts/Results.jsonld">/api/contexts/Results.jsonld</a></li>
J. Fernando Sánchez
7 years ago