def getHistoricFeatures(self, entropy_para):
# this method computes the features that capture the difference between current
# event and background knowledge
end_time = self.getLatestPhotoTime()
begin_time = self.getEarliestPhotoTime()
pi = PhotoInterface()
photos = []
dt = 0
for day in xrange(1, 15):
# here 15 is hard coded because we use 14 days' data as the training
et = end_time - day * 24 * 3600 + dt / 2
bt = begin_time - day * 24 * 3600 - dt / 2
day_photos = pi.rangeQuery(self._event['region'], [str(bt), str(et)])
for photo in day_photos:
# since rangeQuery sorts the photos from the most current to the most early
# thus all the photos in the List "photos" are sorted by their created time from
# the most current to the most early
photos.append(photo)
random.shuffle(photos)
photos = photos[0:min(len(self._event['photos']), len(photos))]
if len(photos) == 0:
# TODO: refine
return [1, 10, 10]
# fake a historic event
historic_event = Event()
historic_event.setPhotos(photos)
historic_event.setRegion(self._event['region'])
historic_event.setActualValue(historic_event._getActualValueByCounting())
historic_event = BaseFeature(historic_event)
# compute the difference between entropy
# this has been smoothed
pro1 = self._divideAndCount(entropy_para)
pro2 = historic_event._divideAndCount(entropy_para)
entropy_divergence = KLDivergence.averageKLDivergence(pro1, pro2)
# compute the difference between top words
topic_divergence = self.computeWordKLDivergenceWith(historic_event)
return [historic_event.getPhotoDisFeatures()[3], topic_divergence,
# historic_event.getEntropy(entropy_para),
entropy_divergence]
def getHistoricFeatures(self, entropy_para):
# this method computes the features that capture the difference between current
# event and background knowledge
end_time = self.getLatestPhotoTime()
begin_time = self.getEarliestPhotoTime()
pi = PhotoInterface()
pi.setDB('citybeat')
pi.setCollection('photos')
photos = []
dt = 0
for day in xrange(1,15):
# here 15 is hard coded because we use 14 days' data as the training
et = end_time - day * 24 * 3600 + dt / 2
bt = begin_time - day * 24 * 3600 - dt / 2
day_photos = pi.rangeQuery(self._event['region'], [str(bt), str(et)])
for photo in day_photos:
# since rangeQuery sorts the photos from the most current to the most early
# thus all the photos in the List "photos" are sorted by their created time from
# the most current to the most early
photos.append(photo)
random.shuffle(photos)
photos = photos[0:min(len(self._event['photos']), len(photos))]
# fake a historic event
historic_event = Event()
historic_event.setPhotos(photos)
historic_event.setRegion(self._event['region'])
historic_event.setActualValue(historic_event._getActualValueByCounting())
historic_event = EventFeature(historic_event)
# compute the difference between entropy
# this has been smoothed
pro1 = self._divideAndCount(entropy_para)
pro2 = historic_event._divideAndCount(entropy_para)
entropy_divergence = KLDivergence.averageKLDivergence(pro1, pro2)
# compute the difference between top words
topic_divergence = self.computeWordKLDivergenceWith(historic_event)
return [historic_event.getPhotoDisFeatures()[3], topic_divergence,
# historic_event.getEntropy(entropy_para),
entropy_divergence]
def getHistoricFeatures(self, entropy_para):
# this method computes the features that capture the difference between current
# event and background knowledge
end_time = self.getLatestPhotoTime()
begin_time = self.getEarliestPhotoTime()
pi = PhotoInterface()
pi.setDB("citybeat")
pi.setCollection("photos")
photos = []
dt = 3600
for day in xrange(1, 15):
# here 15 is hard coded because we use 14 days' data as the training
et = end_time - day * 24 * 3600 + dt / 2
bt = begin_time - day * 24 * 3600 - dt / 2
day_photos = pi.rangeQuery(self._event["region"], [str(bt), str(et)])
for photo in day_photos:
# since rangeQuery sorts the photos from the most current to the most early
# thus all the photos in the List "photos" are sorted by their created time from
# the most current to the most early
photos.append(photo)
event = Event()
event.setPhotos(photos)
event.setRegion(self._event["region"])
event.setActualValue(event.getActualValueByCounting())
event = EventFeature(event)
# compute the difference between entropy
# this has been smoothed
pro1 = self._divideAndCount(entropy_para)
pro2 = event._divideAndCount(entropy_para)
entropy_divergence = KLDivergence.averageKLDivergence(pro1, pro2)
# compute the difference between top words
event_topword_list = self._getTopWords(-1, True)
historic_topword_list = event._getTopWords(-1, True)
n_ind = 0
ind = {}
for word, freq in event_topword_list + historic_topword_list:
if not ind.has_key(word):
ind[word] = n_ind
n_ind += 1
freq1 = [0] * n_ind
freq2 = [0] * n_ind
for word, freq in event_topword_list:
freq1[ind[word]] = freq
for word, freq in historic_topword_list:
freq2[ind[word]] = freq
topic_divergence = KLDivergence.averageKLDivergence(freq1, freq2)
return [
event.getAvgPhotoDis(),
topic_divergence,
# event.getEntropy(entropy_para),
entropy_divergence,
event.getAvgCaptionLen(),
event.getRatioOfPeopleToPhoto(),
]
def getHistoricFeatures(self, entropy_para):
# this method computes the features that capture the difference between current
# event and background knowledge
end_time = self.getLatestPhotoTime()
begin_time = self.getEarliestPhotoTime()
pi = PhotoInterface()
pi.setDB('citybeat')
pi.setCollection('photos')
photos = []
dt = 3600
for day in xrange(1, 15):
# here 15 is hard coded because we use 14 days' data as the training
et = end_time - day * 24 * 3600 + dt / 2
bt = begin_time - day * 24 * 3600 - dt / 2
day_photos = pi.rangeQuery(self._event['region'],
[str(bt), str(et)])
for photo in day_photos:
# since rangeQuery sorts the photos from the most current to the most early
# thus all the photos in the List "photos" are sorted by their created time from
# the most current to the most early
photos.append(photo)
event = Event()
event.setPhotos(photos)
event.setRegion(self._event['region'])
event.setActualValue(event.getActualValueByCounting())
event = EventFeature(event)
# compute the difference between entropy
# this has been smoothed
pro1 = self._divideAndCount(entropy_para)
pro2 = event._divideAndCount(entropy_para)
entropy_divergence = KLDivergence.averageKLDivergence(pro1, pro2)
# compute the difference between top words
event_topword_list = self._getTopWords(-1, True)
historic_topword_list = event._getTopWords(-1, True)
n_ind = 0
ind = {}
for word, freq in event_topword_list + historic_topword_list:
if not ind.has_key(word):
ind[word] = n_ind
n_ind += 1
freq1 = [0] * n_ind
freq2 = [0] * n_ind
for word, freq in event_topword_list:
freq1[ind[word]] = freq
for word, freq in historic_topword_list:
freq2[ind[word]] = freq
topic_divergence = KLDivergence.averageKLDivergence(freq1, freq2)
return [
event.getAvgPhotoDis(),
topic_divergence,
# event.getEntropy(entropy_para),
entropy_divergence,
event.getAvgCaptionLen(),
event.getRatioOfPeopleToPhoto()
]
