def temporalLocalityTemporalDistanceExample(lattice=NEW_YORK):
distances = defaultdict(dict)
for latticeObject in FileIO.iterateJsonFromFile(hashtagsLatticeGraphFile%('training_world','%s_%s'%(2,11))):
if latticeObject['id']==lattice:
latticeHashtagsSet = set(latticeObject['hashtags'])
for neighborLattice, neighborHashtags in latticeObject['links'].iteritems():
distances[neighborLattice] = {}
neighborHashtags = filterOutNeighborHashtagsOutside1_5IQROfTemporalDistance(latticeObject['hashtags'], neighborHashtags, findLag=False)
neighborHashtagsSet = set(neighborHashtags)
distances[neighborLattice]['similarity']=len(latticeHashtagsSet.intersection(neighborHashtagsSet))/float(len(latticeHashtagsSet.union(neighborHashtagsSet)))
distances[neighborLattice]['temporalDistance']=np.mean([abs(latticeObject['hashtags'][k][0]-neighborHashtags[k][0]) for k in neighborHashtags if k in latticeObject['hashtags']])/(60.*60.)
distances[neighborLattice]['geoDistance']=getHaversineDistanceForLids(latticeObject['id'].replace('_', ' '), neighborLattice.replace('_', ' '))
break
dataPoints = []
ax = plt.subplot(111)
for k, data in distances.iteritems(): dataPoints.append((getLocationFromLid(k.replace('_', ' ')), data['temporalDistance']))
points, colors = zip(*sorted(dataPoints, key=itemgetter(1)))
sc = plotPointsOnWorldMap(points, blueMarble=False, bkcolor='#CFCFCF', cmap='RdPu', c=colors, lw = 0, alpha=1.0)
plotPointsOnWorldMap([getLocationFromLid(lattice.replace('_', ' '))], blueMarble=False, bkcolor='#CFCFCF', c='#64FF1C', lw = 0)
divider = make_axes_locatable(ax)
plt.title('Average time difference from New York')
cax = divider.append_axes("right", size="5%", pad=0.05)
plt.colorbar(sc, cax=cax)
# plt.show()
plt.savefig('../images/temporalDistanceExample.png')
def _getDistances():
distances = {}
for latticeObject in FileIO.iterateJsonFromFile(hashtagsLatticeGraphFile%('training_world','%s_%s'%(2,11))):
latticeHashtagsSet = set(latticeObject['hashtags'])
for neighborLattice, neighborHashtags in latticeObject['links'].iteritems():
key = '_'.join(sorted([latticeObject['id'], neighborLattice]))
if key not in distances:
distances[key] = {}
neighborHashtags = filterOutNeighborHashtagsOutside1_5IQROfTemporalDistance(latticeObject['hashtags'], neighborHashtags, findLag=False)
neighborHashtagsSet = set(neighborHashtags)
distances[key]['similarity']=len(latticeHashtagsSet.intersection(neighborHashtagsSet))/float(len(latticeHashtagsSet.union(neighborHashtagsSet)))
distances[key]['temporalDistance']=np.mean([abs(latticeObject['hashtags'][k][0]-neighborHashtags[k][0]) for k in neighborHashtags if k in latticeObject['hashtags']])
distances[key]['geoDistance']=getHaversineDistanceForLids(latticeObject['id'].replace('_', ' '), neighborLattice.replace('_', ' '))
return distances
def sharing_probability_examples(model_ids, kNoOfLocations = 3):
tuo_target_location_and_target_location_label_and_tuo_target_nearby_location_and_nearby_location_label = [
('29.7250_-97.1500', 'austin' ,[('32.6250_-96.4250', 'dallas'), ('29.0000_-97.8750', 'san_antonio'), ('29.7250_-94.9750','houston')]),
]
target_location = '29.7250_-97.1500'
for model_id in model_ids:
tuo_location_and_tuo_neighbor_location_and_sharing_affinity_score \
= Experiments.load_tuo_location_and_tuo_neighbor_location_and_sharing_affinity_score(model_id)
# all_locations = zip(*tuo_location_and_tuo_neighbor_location_and_sharing_affinity_score)[0]
for target_location, target_location_label, tuo_target_nearby_location_and_nearby_location_label in \
tuo_target_location_and_target_location_label_and_tuo_target_nearby_location_and_nearby_location_label:
for location, tuo_neighbor_location_and_sharing_affinity_score in \
tuo_location_and_tuo_neighbor_location_and_sharing_affinity_score:
if location==target_location:
mf_neighbor_location_to_sharing_affinity_score = dict(tuo_neighbor_location_and_sharing_affinity_score)
print [(
nearby_location_label,
getHaversineDistanceForLids(target_nearby_location.replace('_', ' '), location.replace('_', ' ')),
'%0.2f'%mf_neighbor_location_to_sharing_affinity_score[target_nearby_location]
)
for target_nearby_location, nearby_location_label in tuo_target_nearby_location_and_nearby_location_label]
print [(a, '%0.2f'%b)for a,b in tuo_neighbor_location_and_sharing_affinity_score[1:kNoOfLocations+1]]
