Refactored conversion and postprocessing

57-refactor-conversion
J. Fernando Sánchez 6 years ago
parent b48730137d
commit 41aa142ce0

@ -3,10 +3,8 @@ from .models import Error, Results, Entry, from_string
import logging
logger = logging.getLogger(__name__)
boolean = [True, False]
API_PARAMS = {
"algorithm": {
"aliases": ["algorithms", "a", "algo"],
@ -140,6 +138,15 @@ NIF_PARAMS = {
}
}
BUILTIN_PARAMS = {}
for d in [
NIF_PARAMS, CLI_PARAMS, WEB_PARAMS, PLUGINS_PARAMS, EVAL_PARAMS,
API_PARAMS
]:
for k, v in d.items():
BUILTIN_PARAMS[k] = v
def parse_params(indict, *specs):
if not specs:
@ -164,7 +171,7 @@ def parse_params(indict, *specs):
continue
if "options" in options:
if options["options"] == boolean:
outdict[param] = outdict[param] in [None, True, 'true', '1']
outdict[param] = str(outdict[param]).lower() in ['true', '1']
elif outdict[param] not in options["options"]:
wrong_params[param] = spec[param]
if wrong_params:
@ -180,11 +187,19 @@ def parse_params(indict, *specs):
return outdict
def parse_extra_params(request, plugin=None):
def parse_extra_params(request, plugins=None):
plugins = plugins or []
params = request.parameters.copy()
if plugin:
extra_params = parse_params(params, plugin.get('extra_params', {}))
params.update(extra_params)
for plugin in plugins:
if plugin:
extra_params = parse_params(params, plugin.get('extra_params', {}))
for k, v in extra_params.items():
if k not in BUILTIN_PARAMS:
if k in params: # Set by another plugin
del params[k]
else:
params[k] = v
params['{}.{}'.format(plugin.name, k)] = v
return params
@ -194,12 +209,12 @@ def parse_call(params):
params = parse_params(params, NIF_PARAMS)
if params['informat'] == 'text':
results = Results()
entry = Entry(nif__isString=params['input'],
id='#') # Use @base
entry = Entry(nif__isString=params['input'], id='#') # Use @base
results.entries.append(entry)
elif params['informat'] == 'json-ld':
results = from_string(params['input'], cls=Results)
else: # pragma: no cover
raise NotImplementedError('Informat {} is not implemented'.format(params['informat']))
raise NotImplementedError('Informat {} is not implemented'.format(
params['informat']))
results.parameters = params
return results

@ -197,7 +197,9 @@ def api_root(plugin):
plugin = plugin.replace('+', '/')
plugin = plugin.split('/')
req.parameters['algorithm'] = tuple(plugin)
return current_app.senpy.analyse(req)
results = current_app.senpy.analyse(req)
results.analysis = set(i.id for i in results.analysis)
return results
@api_blueprint.route('/evaluate/', methods=['POST', 'GET'])

@ -6,7 +6,6 @@ from future import standard_library
standard_library.install_aliases()
from . import plugins, api
from .plugins import Plugin, evaluate
from .models import Error, AggregatedEvaluation
from .blueprints import api_blueprint, demo_blueprint, ns_blueprint
@ -17,7 +16,6 @@ import copy
import errno
import logging
from . import gsitk_compat
logger = logging.getLogger(__name__)
@ -25,6 +23,7 @@ logger = logging.getLogger(__name__)
class Senpy(object):
""" Default Senpy extension for Flask """
def __init__(self,
app=None,
plugin_folder=".",
@ -50,7 +49,7 @@ class Senpy(object):
self.add_folder('plugins', from_root=True)
else:
# Add only conversion plugins
self.add_folder(os.path.join('plugins', 'conversion'),
self.add_folder(os.path.join('plugins', 'postprocessing'),
from_root=True)
self.app = app
if app is not None:
@ -115,6 +114,7 @@ class Senpy(object):
raise AttributeError("Not a folder or does not exist: %s", folder)
def _get_plugins(self, request):
'''Get a list of plugins that should be run for a specific request'''
if not self.analysis_plugins:
raise Error(
status=404,
@ -132,33 +132,32 @@ class Senpy(object):
plugins = list()
for algo in algos:
algo = algo.lower()
if algo == 'conversion':
continue # Allow 'conversion' as a virtual plugin, which does nothing
if algo not in self._plugins:
msg = ("The algorithm '{}' is not valid\n"
"Valid algorithms: {}").format(algo,
self._plugins.keys())
logger.debug(msg)
raise Error(
status=404,
message=msg)
raise Error(status=404, message=msg)
plugins.append(self._plugins[algo])
return plugins
def _process_entries(self, entries, req, plugins):
def _process(self, req, pending, done=None):
"""
Recursively process the entries with the first plugin in the list, and pass the results
to the rest of the plugins.
"""
if not plugins:
for i in entries:
yield i
return
plugin = plugins[0]
specific_params = api.parse_extra_params(req, plugin)
req.analysis.append({'plugin': plugin,
'parameters': specific_params})
results = plugin.analyse_entries(entries, specific_params)
for i in self._process_entries(results, req, plugins[1:]):
yield i
done = done or []
if not pending:
return req
plugin = pending[0]
results = plugin.process(req, conversions_applied=done)
if plugin not in results.analysis:
results.analysis.append(plugin)
return self._process(results, pending[1:], done)
def install_deps(self):
plugins.install_deps(*self.plugins())
@ -170,72 +169,14 @@ class Senpy(object):
by api.parse_call().
"""
logger.debug("analysing request: {}".format(request))
entries = request.entries
request.entries = []
plugins = self._get_plugins(request)
results = request
for i in self._process_entries(entries, results, plugins):
results.entries.append(i)
self.convert_emotions(results)
logger.debug("Returning analysis result: {}".format(results))
results.analysis = [i['plugin'].id for i in results.analysis]
request.parameters = api.parse_extra_params(request, plugins)
results = self._process(request, plugins)
logger.debug("Got analysis result: {}".format(results))
results = self.postprocess(results)
logger.debug("Returning post-processed result: {}".format(results))
return results
def _get_datasets(self, request):
if not self.datasets:
raise Error(
status=404,
message=("No datasets found."
" Please verify DatasetManager"))
datasets_name = request.parameters.get('dataset', None).split(',')
for dataset in datasets_name:
if dataset not in self.datasets:
logger.debug(("The dataset '{}' is not valid\n"
"Valid datasets: {}").format(dataset,
self.datasets.keys()))
raise Error(
status=404,
message="The dataset '{}' is not valid".format(dataset))
dm = gsitk_compat.DatasetManager()
datasets = dm.prepare_datasets(datasets_name)
return datasets
@property
def datasets(self):
self._dataset_list = {}
dm = gsitk_compat.DatasetManager()
for item in dm.get_datasets():
for key in item:
if key in self._dataset_list:
continue
properties = item[key]
properties['@id'] = key
self._dataset_list[key] = properties
return self._dataset_list
def evaluate(self, params):
logger.debug("evaluating request: {}".format(params))
results = AggregatedEvaluation()
results.parameters = params
datasets = self._get_datasets(results)
plugins = self._get_plugins(results)
for eval in evaluate(plugins, datasets):
results.evaluations.append(eval)
if 'with_parameters' not in results.parameters:
del results.parameters
logger.debug("Returning evaluation result: {}".format(results))
return results
def _conversion_candidates(self, fromModel, toModel):
candidates = self.plugins(plugin_type='emotionConversionPlugin')
for candidate in candidates:
for pair in candidate.onyx__doesConversion:
logging.debug(pair)
if pair['onyx:conversionFrom'] == fromModel \
and pair['onyx:conversionTo'] == toModel:
yield candidate
def convert_emotions(self, resp):
"""
Conversion of all emotions in a response **in place**.
@ -244,11 +185,12 @@ class Senpy(object):
Needless to say, this is far from an elegant solution, but it works.
@todo refactor and clean up
"""
plugins = [i['plugin'] for i in resp.analysis]
plugins = resp.analysis
params = resp.parameters
toModel = params.get('emotionModel', None)
if not toModel:
return
return resp
logger.debug('Asked for model: {}'.format(toModel))
output = params.get('conversion', None)
@ -257,7 +199,8 @@ class Senpy(object):
try:
fromModel = plugin.get('onyx:usesEmotionModel', None)
candidates[plugin.id] = next(self._conversion_candidates(fromModel, toModel))
logger.debug('Analysis plugin {} uses model: {}'.format(plugin.id, fromModel))
logger.debug('Analysis plugin {} uses model: {}'.format(
plugin.id, fromModel))
except StopIteration:
e = Error(('No conversion plugin found for: '
'{} -> {}'.format(fromModel, toModel)),
@ -266,6 +209,7 @@ class Senpy(object):
e.parameters = params
raise e
newentries = []
done = []
for i in resp.entries:
if output == "full":
newemotions = copy.deepcopy(i.emotions)
@ -274,8 +218,7 @@ class Senpy(object):
for j in i.emotions:
plugname = j['prov:wasGeneratedBy']
candidate = candidates[plugname]
resp.analysis.append({'plugin': candidate,
'parameters': params})
done.append({'plugin': candidate, 'parameters': params})
for k in candidate.convert(j, fromModel, toModel, params):
k.prov__wasGeneratedBy = candidate.id
if output == 'nested':
@ -284,12 +227,80 @@ class Senpy(object):
i.emotions = newemotions
newentries.append(i)
resp.entries = newentries
return resp
def _conversion_candidates(self, fromModel, toModel):
candidates = self.plugins(plugin_type=plugins.EmotionConversion)
for candidate in candidates:
for pair in candidate.onyx__doesConversion:
logging.debug(pair)
if candidate.can_convert(fromModel, toModel):
yield candidate
def postprocess(self, response):
'''
Transform the results from the analysis plugins.
It has some pre-defined post-processing like emotion conversion,
and it also allows plugins to auto-select themselves.
'''
response = self.convert_emotions(response)
for plug in self.plugins(plugin_type=plugins.PostProcessing):
if plug.check(response, response.analysis):
response = plug.process(response)
return response
def _get_datasets(self, request):
if not self.datasets:
raise Error(
status=404,
message=("No datasets found."
" Please verify DatasetManager"))
datasets_name = request.parameters.get('dataset', None).split(',')
for dataset in datasets_name:
if dataset not in self.datasets:
logger.debug(("The dataset '{}' is not valid\n"
"Valid datasets: {}").format(
dataset, self.datasets.keys()))
raise Error(
status=404,
message="The dataset '{}' is not valid".format(dataset))
dm = gsitk_compat.DatasetManager()
datasets = dm.prepare_datasets(datasets_name)
return datasets
@property
def datasets(self):
self._dataset_list = {}
dm = gsitk_compat.DatasetManager()
for item in dm.get_datasets():
for key in item:
if key in self._dataset_list:
continue
properties = item[key]
properties['@id'] = key
self._dataset_list[key] = properties
return self._dataset_list
def evaluate(self, params):
logger.debug("evaluating request: {}".format(params))
results = AggregatedEvaluation()
results.parameters = params
datasets = self._get_datasets(results)
plugins = self._get_plugins(results)
for eval in plugins.evaluate(plugins, datasets):
results.evaluations.append(eval)
if 'with_parameters' not in results.parameters:
del results.parameters
logger.debug("Returning evaluation result: {}".format(results))
return results
@property
def default_plugin(self):
if not self._default or not self._default.is_activated:
candidates = self.plugins(plugin_type='analysisPlugin',
is_activated=True)
candidates = self.plugins(
plugin_type='analysisPlugin', is_activated=True)
if len(candidates) > 0:
self._default = candidates[0]
else:
@ -299,7 +310,7 @@ class Senpy(object):
@default_plugin.setter
def default_plugin(self, value):
if isinstance(value, Plugin):
if isinstance(value, plugins.Plugin):
if not value.is_activated:
raise AttributeError('The default plugin has to be activated.')
self._default = value
@ -351,7 +362,8 @@ class Senpy(object):
logger.info("Activating plugin: {}".format(plugin.name))
if sync or not getattr(plugin, 'async', True) or getattr(plugin, 'sync', False):
if sync or not getattr(plugin, 'async', True) or getattr(
plugin, 'sync', False):
return self._activate(plugin)
else:
th = Thread(target=partial(self._activate, plugin))
@ -374,7 +386,8 @@ class Senpy(object):
self._set_active(plugin, False)
if sync or not getattr(plugin, 'async', True) or not getattr(plugin, 'sync', False):
if sync or not getattr(plugin, 'async', True) or not getattr(
plugin, 'sync', False):
self._deactivate(plugin)
else:
th = Thread(target=partial(self._deactivate, plugin))

@ -1,7 +1,6 @@
from future import standard_library
standard_library.install_aliases()
from future.utils import with_metaclass
from functools import partial
@ -10,7 +9,6 @@ import os
import re
import pickle
import logging
import copy
import pprint
import inspect
@ -26,7 +24,6 @@ from .. import api
from .. import gsitk_compat
from .. import testing
logger = logging.getLogger(__name__)
@ -46,16 +43,19 @@ class PluginMeta(models.BaseMeta):
if doc:
attrs['description'] = doc
else:
logger.warn(('Plugin {} does not have a description. '
'Please, add a short summary to help other developers').format(name))
logger.warning(
('Plugin {} does not have a description. '
'Please, add a short summary to help other developers'
).format(name))
cls = super(PluginMeta, mcs).__new__(mcs, name, bases, attrs)
if alias in mcs._classes:
if os.environ.get('SENPY_TESTING', ""):
raise Exception(('The type of plugin {} already exists. '
'Please, choose a different name').format(name))
raise Exception(
('The type of plugin {} already exists. '
'Please, choose a different name').format(name))
else:
logger.warn('Overloading plugin class: {}'.format(alias))
logger.warning('Overloading plugin class: {}'.format(alias))
mcs._classes[alias] = cls
return cls
@ -87,10 +87,12 @@ class Plugin(with_metaclass(PluginMeta, models.Plugin)):
if info:
self.update(info)
self.validate()
self.id = 'endpoint:plugins/{}_{}'.format(self['name'], self['version'])
self.id = 'endpoint:plugins/{}_{}'.format(self['name'],
self['version'])
self.is_activated = False
self._lock = threading.Lock()
self._directory = os.path.abspath(os.path.dirname(inspect.getfile(self.__class__)))
self._directory = os.path.abspath(
os.path.dirname(inspect.getfile(self.__class__)))
data_folder = data_folder or os.getcwd()
subdir = os.path.join(data_folder, self.name)
@ -118,7 +120,8 @@ class Plugin(with_metaclass(PluginMeta, models.Plugin)):
if x not in self:
missing.append(x)
if missing:
raise models.Error('Missing configuration parameters: {}'.format(missing))
raise models.Error(
'Missing configuration parameters: {}'.format(missing))
def get_folder(self):
return os.path.dirname(inspect.getfile(self.__class__))
@ -129,22 +132,60 @@ class Plugin(with_metaclass(PluginMeta, models.Plugin)):
def deactivate(self):
pass
def process(self, request, **kwargs):
"""
An implemented plugin should override this method.
Here, we assume that a process_entries method exists."""
newentries = list(
self.process_entries(request.entries, request.parameters))
request.entries = newentries
return request
def process_entries(self, entries, parameters):
for entry in entries:
self.log.debug('Processing entry with plugin {}: {}'.format(
self, entry))
results = self.process_entry(entry, parameters)
if inspect.isgenerator(results):
for result in results:
yield result
else:
yield results
def process_entry(self, entry, parameters):
"""
This base method is here to adapt plugins which only
implement the *process* function.
Note that this method may yield an annotated entry or a list of
entries (e.g. in a tokenizer)
"""
raise NotImplementedError(
'You need to implement process, process_entries or process_entry in your plugin'
)
def test(self, test_cases=None):
if not test_cases:
if not hasattr(self, 'test_cases'):
raise AttributeError(('Plugin {} [{}] does not have any defined '
'test cases').format(self.id,
inspect.getfile(self.__class__)))
raise AttributeError(
('Plugin {} [{}] does not have any defined '
'test cases').format(self.id,
inspect.getfile(self.__class__)))
test_cases = self.test_cases
for case in test_cases:
try:
self.test_case(case)
self.log.debug('Test case passed:\n{}'.format(pprint.pformat(case)))
self.log.debug('Test case passed:\n{}'.format(
pprint.pformat(case)))
except Exception as ex:
self.log.warn('Test case failed:\n{}'.format(pprint.pformat(case)))
self.log.warning('Test case failed:\n{}'.format(
pprint.pformat(case)))
raise
def test_case(self, case, mock=testing.MOCK_REQUESTS):
if 'entry' not in case and 'input' in case:
entry = models.Entry(_auto_id=False)
entry.nif__isString = case['input']
case['entry'] = entry
entry = models.Entry(case['entry'])
given_parameters = case.get('params', case.get('parameters', {}))
expected = case.get('expected', None)
@ -152,21 +193,25 @@ class Plugin(with_metaclass(PluginMeta, models.Plugin)):
responses = case.get('responses', [])
try:
params = api.parse_params(given_parameters, self.extra_params)
request = models.Response()
request.parameters = api.parse_params(given_parameters,
self.extra_params)
request.entries = [
entry,
]
method = partial(self.analyse_entries, [entry, ], params)
method = partial(self.process, request)
if mock:
res = list(method())
res = method()
else:
with testing.patch_all_requests(responses):
res = list(method())
res = method()
if not isinstance(expected, list):
expected = [expected]
utils.check_template(res, expected)
for r in res:
r.validate()
utils.check_template(res.entries, expected)
res.validate()
except models.Error:
if should_fail:
return
@ -203,40 +248,26 @@ class Analysis(Plugin):
A subclass of Plugin that analyses text and provides an annotation.
'''
def analyse(self, *args, **kwargs):
raise NotImplementedError(
'Your plugin should implement either analyse or analyse_entry')
def analyse(self, request, parameters):
return super(Analysis, self).process(request)
def analyse_entry(self, entry, parameters):
""" An implemented plugin should override this method.
This base method is here to adapt old style plugins which only
implement the *analyse* function.
Note that this method may yield an annotated entry or a list of
entries (e.g. in a tokenizer)
"""
text = entry['nif:isString']
params = copy.copy(parameters)
params['input'] = text
results = self.analyse(**params)
for i in results.entries:
def analyse_entries(self, entries, parameters):
for i in super(Analysis, self).process_entries(entries, parameters):
yield i
def analyse_entries(self, entries, parameters):
for entry in entries:
self.log.debug('Analysing entry with plugin {}: {}'.format(self, entry))
results = self.analyse_entry(entry, parameters)
if inspect.isgenerator(results):
for result in results:
yield result
else:
yield results
def process(self, request, **kwargs):
return self.analyse(request, request.parameters)
def test_case(self, case):
if 'entry' not in case and 'input' in case:
entry = models.Entry(_auto_id=False)
entry.nif__isString = case['input']
case['entry'] = entry
super(Analysis, self).test_case(case)
def process_entries(self, entries, parameters):
for i in self.analyse_entries(entries, parameters):
yield i
def process_entry(self, entry, parameters, **kwargs):
if hasattr(self, 'analyse_entry'):
for i in self.analyse_entry(entry, parameters):
yield i
else:
super(Analysis, self).process_entry(entry, parameters, **kwargs)
AnalysisPlugin = Analysis
@ -247,7 +278,20 @@ class Conversion(Plugin):
A subclass of Plugins that convert between different annotation models.
e.g. a conversion of emotion models, or normalization of sentiment values.
'''
pass
def process(self, response, plugins=None, **kwargs):
plugins = plugins or []
newentries = []
for entry in response.entries:
newentries.append(
self.convert_entry(entry, response.parameters, plugins))
response.entries = newentries
return response
def convert_entry(self, entry, parameters, conversions_applied):
raise NotImplementedError(
'You should implement a way to convert each entry, or a custom process method'
)
ConversionPlugin = Conversion
@ -284,12 +328,28 @@ class EmotionConversion(Conversion):
'''
A subclass of Conversion that converts emotion annotations using different models
'''
pass
def can_convert(self, fromModel, toModel):
'''
Whether this plugin can convert from fromModel to toModel.
If fromModel is None, it is interpreted as "any Model"
'''
for pair in self.onyx__doesConversion:
if (pair['onyx:conversionTo'] == toModel) and \
((fromModel is None) or (pair['onyx:conversionFrom'] == fromModel)):
return True
return False
EmotionConversionPlugin = EmotionConversion
class PostProcessing(Plugin):
def check(self, request, plugins):
'''Should this plugin be run for this request?'''
return False
class Box(AnalysisPlugin):
'''
Black box plugins delegate analysis to a function.
@ -314,9 +374,10 @@ class Box(AnalysisPlugin):
return output
def predict_one(self, input):
raise NotImplementedError('You should define the behavior of this plugin')
raise NotImplementedError(
'You should define the behavior of this plugin')
def analyse_entries(self, entries, params):
def process_entries(self, entries, params):
for entry in entries:
input = self.input(entry=entry, params=params)
results = self.predict_one(input=input)
@ -385,7 +446,6 @@ class EmotionBox(TextBox, EmotionPlugin):
class MappingMixin(object):
@property
def mappings(self):
return self._mappings
@ -395,11 +455,10 @@ class MappingMixin(object):
self._mappings = value
def output(self, output, entry, params):
output = self.mappings.get(output,
self.mappings.get('default', output))
return super(MappingMixin, self).output(output=output,
entry=entry,
params=params)
output = self.mappings.get(output, self.mappings.get(
'default', output))
return super(MappingMixin, self).output(
output=output, entry=entry, params=params)
class ShelfMixin(object):
@ -412,7 +471,8 @@ class ShelfMixin(object):
with self.open(self.shelf_file, 'rb') as p:
self._sh = pickle.load(p)
except (IndexError, EOFError, pickle.UnpicklingError):
self.log.warning('Corrupted shelf file: {}'.format(self.shelf_file))
self.log.warning('Corrupted shelf file: {}'.format(
self.shelf_file))
if not self.get('force_shelf', False):
raise
return self._sh
@ -460,8 +520,7 @@ def pfilter(plugins, plugin_type=Analysis, **kwargs):
plugin_type = plugin_type[0].upper() + plugin_type[1:]
pclass = globals()[plugin_type]
logger.debug('Class: {}'.format(pclass))
candidates = filter(lambda x: isinstance(x, pclass),
plugins)
candidates = filter(lambda x: isinstance(x, pclass), plugins)
except KeyError:
raise models.Error('{} is not a valid type'.format(plugin_type))
else:
@ -471,8 +530,7 @@ def pfilter(plugins, plugin_type=Analysis, **kwargs):
def matches(plug):
res = all(getattr(plug, k, None) == v for (k, v) in kwargs.items())
logger.debug(
"matching {} with {}: {}".format(plug.name, kwargs, res))
logger.debug("matching {} with {}: {}".format(plug.name, kwargs, res))
return res
if kwargs:
@ -506,14 +564,14 @@ def install_deps(*plugins):
for req in requirements:
pip_args.append(req)
logger.info('Installing requirements: ' + str(requirements))
process = subprocess.Popen(pip_args,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
process = subprocess.Popen(
pip_args, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
_log_subprocess_output(process)
exitcode = process.wait()
installed = True
if exitcode != 0:
raise models.Error("Dependencies not properly installed: {}".format(pip_args))
raise models.Error(
"Dependencies not properly installed: {}".format(pip_args))
nltk_resources |= set(info.get('nltk_resources', []))
installed |= nltk.download(list(nltk_resources))
@ -556,7 +614,7 @@ def from_folder(folders, loader=from_path, **kwargs):
def from_info(info, root=None, install_on_fail=True, **kwargs):
if any(x not in info for x in ('module',)):
if any(x not in info for x in ('module', )):
raise ValueError('Plugin info is not valid: {}'.format(info))
module = info["module"]
@ -593,7 +651,8 @@ def one_from_module(module, root, info, **kwargs):
if '@type' in info:
cls = PluginMeta.from_type(info['@type'])
return cls(info=info, **kwargs)
instance = next(from_module(module=module, root=root, info=info, **kwargs), None)
instance = next(
from_module(module=module, root=root, info=info, **kwargs), None)
if not instance:
raise Exception("No valid plugin for: {}".format(module))
return instance
@ -617,7 +676,8 @@ def _instances_in_module(module):
def _from_module_name(module, root, info=None, **kwargs):
module = load_module(module, root)
for plugin in _from_loaded_module(module=module, root=root, info=info, **kwargs):
for plugin in _from_loaded_module(
module=module, root=root, info=info, **kwargs):
yield plugin
@ -629,9 +689,10 @@ def _from_loaded_module(module, info=None, **kwargs):
def evaluate(plugins, datasets, **kwargs):
ev = gsitk_compat.Eval(tuples=None,
datasets=datasets,
pipelines=[plugin.as_pipe() for plugin in plugins])
ev = gsitk_compat.Eval(
tuples=None,
datasets=datasets,
pipelines=[plugin.as_pipe() for plugin in plugins])
ev.evaluate()
results = ev.results
evaluations = evaluations_to_JSONLD(results, **kwargs)

@ -1,6 +1,6 @@
---
name: Ekman2FSRE
module: senpy.plugins.conversion.emotion.centroids
module: senpy.plugins.postprocessing.emotion.centroids
description: Plugin to convert emotion sets from Ekman to VAD
version: 0.2
# No need to specify onyx:doesConversion because centroids.py adds it automatically from centroids_direction

@ -1,6 +1,6 @@
---
name: Ekman2PAD
module: senpy.plugins.conversion.emotion.centroids
module: senpy.plugins.postprocessing.emotion.centroids
description: Plugin to convert emotion sets from Ekman to VAD
version: 0.2
# No need to specify onyx:doesConversion because centroids.py adds it automatically from centroids_direction

@ -0,0 +1,196 @@
from senpy import PostProcessing, easy_test
class MaxEmotion(PostProcessing):
'''Plugin to extract the emotion with highest value from an EmotionSet'''
author = '@dsuarezsouto'
version = '0.1'
def process_entry(self, entry, params):
if len(entry.emotions) < 1:
yield entry
return
set_emotions = entry.emotions[0]['onyx:hasEmotion']
# If there is only one emotion, do not modify it
if len(set_emotions) < 2:
yield entry
return
max_emotion = set_emotions[0]
# Extract max emotion from the set emotions (emotion with highest intensity)
for tmp_emotion in set_emotions:
if tmp_emotion['onyx:hasEmotionIntensity'] > max_emotion[
'onyx:hasEmotionIntensity']:
max_emotion = tmp_emotion
if max_emotion['onyx:hasEmotionIntensity'] == 0:
max_emotion['onyx:hasEmotionCategory'] = "neutral"
max_emotion['onyx:hasEmotionIntensity'] = 1.0
entry.emotions[0]['onyx:hasEmotion'] = [max_emotion]
entry.emotions[0]['prov:wasGeneratedBy'] = "maxSentiment"
yield entry
def check(self, request, plugins):
return 'maxemotion' in request.parameters and self not in plugins
# Test Cases:
# 1 Normal Situation.
# 2 Case to return a Neutral Emotion.
test_cases = [
{
"name":
"If there are several emotions within an emotion set, reduce it to one.",
"entry": {
"@type":
"entry",
"emotions": [
{
"@id":
"Emotions0",
"@type":
"emotionSet",
"onyx:hasEmotion": [
{
"@id": "_:Emotion_1538121033.74",
"@type": "emotion",
"onyx:hasEmotionCategory": "anger",
"onyx:hasEmotionIntensity": 0
},
{
"@id": "_:Emotion_1538121033.74",
"@type": "emotion",
"onyx:hasEmotionCategory": "joy",
"onyx:hasEmotionIntensity": 0.3333333333333333
},
{
"@id": "_:Emotion_1538121033.74",
"@type": "emotion",
"onyx:hasEmotionCategory": "negative-fear",
"onyx:hasEmotionIntensity": 0
},
{
"@id": "_:Emotion_1538121033.74",
"@type": "emotion",
"onyx:hasEmotionCategory": "sadness",
"onyx:hasEmotionIntensity": 0
},
{
"@id": "_:Emotion_1538121033.74",
"@type": "emotion",
"onyx:hasEmotionCategory": "disgust",
"onyx:hasEmotionIntensity": 0
}
]
}
],
"nif:isString":
"Test"
},
'expected': {
"@type":
"entry",
"emotions": [
{
"@id":
"Emotions0",
"@type":
"emotionSet",
"onyx:hasEmotion": [
{
"@id": "_:Emotion_1538121033.74",
"@type": "emotion",
"onyx:hasEmotionCategory": "joy",
"onyx:hasEmotionIntensity": 0.3333333333333333
}
],
"prov:wasGeneratedBy":
'maxSentiment'
}
],
"nif:isString":
"Test"
}
},
{
"name":
"If the maximum emotion has an intensity of 0, return a neutral emotion.",
"entry": {
"@type":
"entry",
"emotions": [{
"@id":
"Emotions0",
"@type":
"emotionSet",
"onyx:hasEmotion": [
{
"@id": "_:Emotion_1538121033.74",
"@type": "emotion",
"onyx:hasEmotionCategory": "anger",
"onyx:hasEmotionIntensity": 0
},
{
"@id": "_:Emotion_1538121033.74",
"@type": "emotion",
"onyx:hasEmotionCategory": "joy",
"onyx:hasEmotionIntensity": 0
},
{
"@id":
"_:Emotion_1538121033.74",
"@type":
"emotion",
"onyx:hasEmotionCategory":
"negative-fear",
"onyx:hasEmotionIntensity":
0
},
{
"@id": "_:Emotion_1538121033.74",
"@type": "emotion",
"onyx:hasEmotionCategory":
"sadness",
"onyx:hasEmotionIntensity": 0
},
{
"@id": "_:Emotion_1538121033.74",
"@type": "emotion",
"onyx:hasEmotionCategory":
"disgust",
"onyx:hasEmotionIntensity": 0
}]
}],
"nif:isString":
"Test"
},
'expected': {
"@type":
"entry",
"emotions": [{
"@id":
"Emotions0",
"@type":
"emotionSet",
"onyx:hasEmotion": [{
"@id": "_:Emotion_1538121033.74",
"@type": "emotion",
"onyx:hasEmotionCategory": "neutral",
"onyx:hasEmotionIntensity": 1
}],
"prov:wasGeneratedBy":
'maxSentiment'
}],
"nif:isString":
"Test"
}
}
]
if __name__ == '__main__':
easy_test()

@ -138,14 +138,14 @@ class BlueprintsTest(TestCase):
# Calling dummy twice, should return the same string
self.assertCode(resp, 200)
js = parse_resp(resp)
assert len(js['analysis']) == 2
assert len(js['analysis']) == 1
assert js['entries'][0]['nif:isString'] == 'My aloha mohame'
resp = self.client.get("/api/Dummy+Dummy?i=My aloha mohame")
# Same with pluses instead of slashes
self.assertCode(resp, 200)
js = parse_resp(resp)
assert len(js['analysis']) == 2
assert len(js['analysis']) == 1
assert js['entries'][0]['nif:isString'] == 'My aloha mohame'
def test_error(self):

@ -121,8 +121,8 @@ class ExtensionsTest(TestCase):
# Leaf (defaultdict with __setattr__ and __getattr__.
r1 = analyse(self.senpy, algorithm="Dummy", input="tupni", output="tuptuo")
r2 = analyse(self.senpy, input="tupni", output="tuptuo")
assert r1.analysis[0] == "endpoint:plugins/Dummy_0.1"
assert r2.analysis[0] == "endpoint:plugins/Dummy_0.1"
assert r1.analysis[0].id == "endpoint:plugins/Dummy_0.1"
assert r2.analysis[0].id == "endpoint:plugins/Dummy_0.1"
assert r1.entries[0]['nif:isString'] == 'input'
def test_analyse_empty(self):
@ -156,8 +156,8 @@ class ExtensionsTest(TestCase):
r2 = analyse(self.senpy,
input="tupni",
output="tuptuo")
assert r1.analysis[0] == "endpoint:plugins/Dummy_0.1"
assert r2.analysis[0] == "endpoint:plugins/Dummy_0.1"
assert r1.analysis[0].id == "endpoint:plugins/Dummy_0.1"
assert r2.analysis[0].id == "endpoint:plugins/Dummy_0.1"
assert r1.entries[0]['nif:isString'] == 'input'
def test_analyse_error(self):
@ -165,7 +165,7 @@ class ExtensionsTest(TestCase):
mm.id = 'magic_mock'
mm.name = 'mock'
mm.is_activated = True
mm.analyse_entries.side_effect = Error('error in analysis', status=500)
mm.process.side_effect = Error('error in analysis', status=500)
self.senpy.add_plugin(mm)
try:
analyse(self.senpy, input='nothing', algorithm='MOCK')
@ -175,8 +175,7 @@ class ExtensionsTest(TestCase):
assert ex['status'] == 500
ex = Exception('generic exception on analysis')
mm.analyse.side_effect = ex
mm.analyse_entries.side_effect = ex
mm.process.side_effect = ex
try:
analyse(self.senpy, input='nothing', algorithm='MOCK')
@ -211,27 +210,28 @@ class ExtensionsTest(TestCase):
'emoml:valence': 0
}))
response = Results({
'analysis': [{'plugin': plugin}],
'analysis': [plugin],
'entries': [Entry({
'nif:isString': 'much ado about nothing',
'emotions': [eSet1]
})]
})
params = {'emotionModel': 'emoml:big6',
'algorithm': ['conversion'],
'conversion': 'full'}
r1 = deepcopy(response)
r1.parameters = params
self.senpy.convert_emotions(r1)
self.senpy.analyse(r1)
assert len(r1.entries[0].emotions) == 2
params['conversion'] = 'nested'
r2 = deepcopy(response)
r2.parameters = params
self.senpy.convert_emotions(r2)
self.senpy.analyse(r2)
assert len(r2.entries[0].emotions) == 1
assert r2.entries[0].emotions[0]['prov:wasDerivedFrom'] == eSet1
params['conversion'] = 'filtered'
r3 = deepcopy(response)
r3.parameters = params
self.senpy.convert_emotions(r3)
self.senpy.analyse(r3)
assert len(r3.entries[0].emotions) == 1
r3.jsonld()

@ -8,7 +8,7 @@ import tempfile
from unittest import TestCase, skipIf
from senpy.models import Results, Entry, EmotionSet, Emotion, Plugins
from senpy import plugins
from senpy.plugins.conversion.emotion.centroids import CentroidConversion
from senpy.plugins.postprocessing.emotion.centroids import CentroidConversion
from senpy.gsitk_compat import GSITK_AVAILABLE
import pandas as pd

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