mirror of https://github.com/gsi-upm/senpy
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
103 lines
4.1 KiB
Python
103 lines
4.1 KiB
Python
from senpy.plugins import EmotionConversionPlugin
|
|
from senpy.models import EmotionSet, Emotion, Error
|
|
|
|
import logging
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class CentroidConversion(EmotionConversionPlugin):
|
|
def __init__(self, info):
|
|
if 'centroids' not in info:
|
|
raise Error('Centroid conversion plugins should provide '
|
|
'the centroids in their senpy file')
|
|
if 'onyx:doesConversion' not in info:
|
|
if 'centroids_direction' not in info:
|
|
raise Error('Please, provide centroids direction')
|
|
|
|
cf, ct = info['centroids_direction']
|
|
info['onyx:doesConversion'] = [{
|
|
'onyx:conversionFrom': cf,
|
|
'onyx:conversionTo': ct
|
|
}, {
|
|
'onyx:conversionFrom': ct,
|
|
'onyx:conversionTo': cf
|
|
}]
|
|
|
|
if 'aliases' in info:
|
|
aliases = info['aliases']
|
|
ncentroids = {}
|
|
for k1, v1 in info['centroids'].items():
|
|
nv1 = {}
|
|
for k2, v2 in v1.items():
|
|
nv1[aliases.get(k2, k2)] = v2
|
|
ncentroids[aliases.get(k1, k1)] = nv1
|
|
info['centroids'] = ncentroids
|
|
|
|
super(CentroidConversion, self).__init__(info)
|
|
|
|
self.dimensions = set()
|
|
for c in self.centroids.values():
|
|
self.dimensions.update(c.keys())
|
|
self.neutralPoints = self.get("neutralPoints", dict())
|
|
if not self.neutralPoints:
|
|
for i in self.dimensions:
|
|
self.neutralPoints[i] = self.get("neutralValue", 0)
|
|
|
|
def _forward_conversion(self, original):
|
|
"""Sum the VAD value of all categories found weighted by intensity.
|
|
Intensities are scaled by onyx:maxIntensityValue if it is present, else maxIntensityValue
|
|
is assumed to be one. Emotion entries that do not have onxy:hasEmotionIntensity specified
|
|
are assumed to have maxIntensityValue. Emotion entries that do not have
|
|
onyx:hasEmotionCategory specified are ignored."""
|
|
res = Emotion()
|
|
maxIntensity = float(original.get("onyx:maxIntensityValue", 1))
|
|
for e in original.onyx__hasEmotion:
|
|
category = e.get("onyx:hasEmotionCategory", None)
|
|
if not category:
|
|
continue
|
|
intensity = e.get("onyx:hasEmotionIntensity", maxIntensity) / maxIntensity
|
|
if not intensity:
|
|
continue
|
|
centroid = self.centroids.get(category, None)
|
|
if centroid:
|
|
for dim, value in centroid.items():
|
|
neutral = self.neutralPoints[dim]
|
|
if dim not in res:
|
|
res[dim] = 0
|
|
res[dim] += (value - neutral) * intensity + neutral
|
|
return res
|
|
|
|
def _backwards_conversion(self, original):
|
|
"""Find the closest category"""
|
|
centroids = self.centroids
|
|
neutralPoints = self.neutralPoints
|
|
dimensions = self.dimensions
|
|
|
|
def distance_k(centroid, original, k):
|
|
# k component of the distance between the value and a given centroid
|
|
return (centroid.get(k, neutralPoints[k]) - original.get(k, neutralPoints[k]))**2
|
|
|
|
def distance(centroid):
|
|
return sum(distance_k(centroid, original, k) for k in dimensions)
|
|
|
|
emotion = min(centroids, key=lambda x: distance(centroids[x]))
|
|
|
|
result = Emotion(onyx__hasEmotionCategory=emotion)
|
|
result.onyx__algorithmConfidence = distance(centroids[emotion])
|
|
return result
|
|
|
|
def convert(self, emotionSet, fromModel, toModel, params):
|
|
|
|
cf, ct = self.centroids_direction
|
|
logger.debug(
|
|
'{}\n{}\n{}\n{}'.format(emotionSet, fromModel, toModel, params))
|
|
e = EmotionSet()
|
|
if fromModel == cf and toModel == ct:
|
|
e.onyx__hasEmotion.append(self._forward_conversion(emotionSet))
|
|
elif fromModel == ct and toModel == cf:
|
|
for i in emotionSet.onyx__hasEmotion:
|
|
e.onyx__hasEmotion.append(self._backwards_conversion(i))
|
|
else:
|
|
raise Error('EMOTION MODEL NOT KNOWN')
|
|
yield e
|