mirror of
https://github.com/gsi-upm/senpy
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163 lines
6.2 KiB
ReStructuredText
163 lines
6.2 KiB
ReStructuredText
Developing new plugins
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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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Plugins Interface
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=======
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- Definition file, has the ".senpy" extension.
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- Code file, is a python file.
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Plugins Definitions
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===================
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The definition file can be written in JSON or YAML, where the data representation consists on attribute-value pairs.
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The principal attributes are:
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* name: plugin name used in senpy to call the plugin.
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* module: indicates the module that will be loaded
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.. code:: python
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{
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"name" : "senpyPlugin",
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"module" : "{python code file}"
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}
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.. code:: python
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name: senpyPlugin
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module: {python code file}
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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: called in every user requests. It takes in the parameters supplied by a user and should return a senpy Response.
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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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F.A.Q.
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======
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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(self, **params):
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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": params["input"]}]
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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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response = Results(prefix=p)
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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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entry = Entry(id="Entry0",
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nif__isString=params["input"])
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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 = []
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entry.sentiments.append(sentiment)
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entry.language = lang
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response.entries.append(entry)
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return response
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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 extracture of attribute-value pair.
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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.
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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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python -m senpy -d
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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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