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https://github.com/gsi-upm/senpy
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Filter conversion plugins
Closes #12 * Shows only analysis plugins by default on /api/plugins * Adds a plugin_type parameter to get other types of plugins * default_plugin chosen from analysis plugins
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@ -26,6 +26,13 @@ API_PARAMS = {
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"aliases": ["emotionModel", "emoModel"],
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"aliases": ["emotionModel", "emoModel"],
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"required": False
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"required": False
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},
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},
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"plugin_type": {
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"@id": "pluginType",
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"description": 'What kind of plugins to list',
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"aliases": ["pluginType", "plugin_type"],
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"required": True,
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"default": "analysisPlugin"
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},
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"conversion": {
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"conversion": {
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"@id": "conversion",
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"@id": "conversion",
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"description": "How to show the elements that have (not) been converted",
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"description": "How to show the elements that have (not) been converted",
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@ -121,7 +121,9 @@ def api():
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@basic_api
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@basic_api
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def plugins():
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def plugins():
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sp = current_app.senpy
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sp = current_app.senpy
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dic = Plugins(plugins=list(sp.plugins.values()))
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ptype = request.params.get('plugin_type')
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plugins = sp.filter_plugins(plugin_type=ptype)
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dic = Plugins(plugins=list(plugins.values()))
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return dic
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return dic
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@ -5,7 +5,8 @@ It orchestrates plugin (de)activation and analysis.
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from future import standard_library
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from future import standard_library
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standard_library.install_aliases()
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standard_library.install_aliases()
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from .plugins import SentimentPlugin, SenpyPlugin
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from . import plugins
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from .plugins import SenpyPlugin
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from .models import Error, Entry, Results
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from .models import Error, Entry, Results
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from .blueprints import api_blueprint, demo_blueprint, ns_blueprint
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from .blueprints import api_blueprint, demo_blueprint, ns_blueprint
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from .api import API_PARAMS, NIF_PARAMS, parse_params
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from .api import API_PARAMS, NIF_PARAMS, parse_params
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@ -367,6 +368,22 @@ class Senpy(object):
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def filter_plugins(self, **kwargs):
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def filter_plugins(self, **kwargs):
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""" Filter plugins by different criteria """
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""" Filter plugins by different criteria """
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ptype = kwargs.pop('plugin_type', None)
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logger.debug('#' * 100)
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logger.debug('ptype {}'.format(ptype))
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if ptype:
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try:
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ptype = ptype[0].upper() + ptype[1:]
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pclass = getattr(plugins, ptype)
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logger.debug('Class: {}'.format(pclass))
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candidates = filter(lambda x: isinstance(x, pclass),
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self.plugins.values())
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except AttributeError:
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raise Error('{} is not a valid type'.format(ptype))
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else:
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candidates = self.plugins.values()
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logger.debug(candidates)
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def matches(plug):
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def matches(plug):
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res = all(getattr(plug, k, None) == v for (k, v) in kwargs.items())
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res = all(getattr(plug, k, None) == v for (k, v) in kwargs.items())
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@ -374,15 +391,11 @@ class Senpy(object):
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"matching {} with {}: {}".format(plug.name, kwargs, res))
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"matching {} with {}: {}".format(plug.name, kwargs, res))
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return res
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return res
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if not kwargs:
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if kwargs:
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return self.plugins
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candidates = filter(matches, candidates)
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else:
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return {p.name: p for p in candidates}
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return {n: p for n, p in self.plugins.items() if matches(p)}
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def sentiment_plugins(self):
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@property
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""" Return only the sentiment plugins """
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def analysis_plugins(self):
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return {
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""" Return only the analysis plugins """
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p: plugin
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return self.filter_plugins(plugin_type='analysisPlugin')
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for p, plugin in self.plugins.items()
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if isinstance(plugin, SentimentPlugin)
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}
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@ -30,6 +30,15 @@ class SenpyPlugin(models.Plugin):
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def get_folder(self):
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def get_folder(self):
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return os.path.dirname(inspect.getfile(self.__class__))
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return os.path.dirname(inspect.getfile(self.__class__))
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def activate(self):
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pass
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def deactivate(self):
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pass
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class AnalysisPlugin(SenpyPlugin):
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def analyse(self, *args, **kwargs):
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def analyse(self, *args, **kwargs):
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raise NotImplemented(
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raise NotImplemented(
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'Your method should implement either analyse or analyse_entry')
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'Your method should implement either analyse or analyse_entry')
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@ -48,30 +57,27 @@ class SenpyPlugin(models.Plugin):
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for i in results.entries:
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for i in results.entries:
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yield i
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yield i
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def activate(self):
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pass
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def deactivate(self):
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class ConversionPlugin(SenpyPlugin):
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pass
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pass
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class SentimentPlugin(models.SentimentPlugin, SenpyPlugin):
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class SentimentPlugin(models.SentimentPlugin, AnalysisPlugin):
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def __init__(self, info, *args, **kwargs):
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def __init__(self, info, *args, **kwargs):
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super(SentimentPlugin, self).__init__(info, *args, **kwargs)
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super(SentimentPlugin, self).__init__(info, *args, **kwargs)
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self.minPolarityValue = float(info.get("minPolarityValue", 0))
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self.minPolarityValue = float(info.get("minPolarityValue", 0))
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self.maxPolarityValue = float(info.get("maxPolarityValue", 1))
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self.maxPolarityValue = float(info.get("maxPolarityValue", 1))
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class EmotionPlugin(models.EmotionPlugin, SenpyPlugin):
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class EmotionPlugin(models.EmotionPlugin, AnalysisPlugin):
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def __init__(self, info, *args, **kwargs):
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def __init__(self, info, *args, **kwargs):
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super(EmotionPlugin, self).__init__(info, *args, **kwargs)
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super(EmotionPlugin, self).__init__(info, *args, **kwargs)
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self.minEmotionValue = float(info.get("minEmotionValue", -1))
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self.minEmotionValue = float(info.get("minEmotionValue", -1))
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self.maxEmotionValue = float(info.get("maxEmotionValue", 1))
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self.maxEmotionValue = float(info.get("maxEmotionValue", 1))
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class EmotionConversionPlugin(models.EmotionConversionPlugin, SenpyPlugin):
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class EmotionConversionPlugin(models.EmotionConversionPlugin, ConversionPlugin):
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def __init__(self, info, *args, **kwargs):
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pass
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super(EmotionConversionPlugin, self).__init__(info, *args, **kwargs)
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class ShelfMixin(object):
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class ShelfMixin(object):
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