def cate_smooth(twt_lst, ratio, sel, lmd):
"""Smoothing the dataset by place category"""
rst_lst = dataset.Dataset()
pid_lst = twt_lst.distinct('place_id')
twt_dist = twt_lst.groupfunc('place_id', len)
tid_set = set(twt_lst.distinct('place_id'))
pid_set = set(pid_lst)
for pid in pid_lst:
plc = dataset.loadrows(GEOTWEET, ('id', 'lat', 'lng', 'super_category'), \
('id = \'{0}\''.format(pid),), 'place')
plc_type = plc[0]['super_category']
tmp_lst = list()
cand = dataset.type_random(plc_type)
for twt in cand:
if twt['id'] not in tid_set and twt['place_id'] not in pid_lst:
if sel(twt, plc):
twt['place_id'] = pid
tid_set.add(twt['id'])
pid_set.add(twt['place_id'])
tmp_lst.append(twt)
if len(tmp_lst) >= ratio * twt_dist[pid]: break
rst_lst.extend(tmp_lst)
rst_lst.extend(twt_lst)
return rst_lst
def cnt_map(region, table = 'sample', draw = True):
"""Draw a region map of tweets"""
twt_lst = dataset.loadrows(GEOTWEET, ('lat', 'lng'),
('MBRContains({0}, geo)'.format(dataset.geo_rect(*region)),), table)
lat = list();
lng = list();
for twt in twt_lst:
lat.append(twt['lat'])
lng.append(twt['lng'])
if draw:
x = np.array(lng)
y = np.array(lat)
xmin = x.min()
xmax = x.max()
ymin = y.min()
ymax = y.max()
plt.hexbin(x,y, gridsize=200, cmap=cm.jet)
plt.axis([xmin, xmax, ymin, ymax])
plt.title("Hexagon binning")
cb = plt.colorbar()
cb.set_label('counts')
plt.show()
return lat, lng
def cate_smooth(twt_lst, ratio, sel, lmd):
"""Smoothing the dataset by place category"""
rst_lst = dataset.Dataset()
pid_lst = twt_lst.distinct('place_id')
twt_dist = twt_lst.groupfunc('place_id', len)
tid_set = set(twt_lst.distinct('place_id'))
pid_set = set(pid_lst)
for pid in pid_lst:
plc = dataset.loadrows(GEOTWEET, ('id', 'lat', 'lng', 'super_category'), \
('id = \'{0}\''.format(pid),), 'place')
plc_type = plc[0]['super_category']
tmp_lst = list()
cand = dataset.type_random(plc_type)
for twt in cand:
if twt['id'] not in tid_set and twt['place_id'] not in pid_lst:
if sel(twt, plc):
twt['place_id'] = pid
tid_set.add(twt['id'])
pid_set.add(twt['place_id'])
tmp_lst.append(twt)
if len(tmp_lst) >= ratio * twt_dist[pid]: break
rst_lst.extend(tmp_lst)
rst_lst.extend(twt_lst)
return rst_lst
def region2arff(dst, region):
"""Generate data in the region in arff format"""
twt_lst = dataset.loadrows(GEOTWEET, ("place_id", "text"), ("MBRContains({0}, geo)".format(geo_rect(*region)),))
vec_lst = dataset.Dataset([line2tf(comma_filter(fourq_filter(twt["text"]))) for twt in twt_lst])
bgdist = vec_lst.bgdist()
# cut all the key that only appear less than 3 times
keylist = [key for key in bgdist.iterkeys() if bgdist[key] > 3]
keylist.append("__CLASS__")
for i in range(len(vec_lst)):
vec_lst[i]["__CLASS__"] = twt_lst[i]["place_id"]
vec_lst.gen_arff(dst, keylist)
def region2arff(dst, region):
"""Generate data in the region in arff format"""
twt_lst = dataset.loadrows(
GEOTWEET, ('place_id', 'text'),
('MBRContains({0}, geo)'.format(geo_rect(*region)), ))
vec_lst = dataset.Dataset([line2tf(comma_filter(fourq_filter(twt['text']))) \
for twt in twt_lst])
bgdist = vec_lst.bgdist()
#cut all the key that only appear less than 3 times
keylist = [key for key in bgdist.iterkeys() if bgdist[key] > 3]
keylist.append('__CLASS__')
for i in range(len(vec_lst)):
vec_lst[i]['__CLASS__'] = twt_lst[i]['place_id']
vec_lst.gen_arff(dst, keylist)
def time_plot(place_id):
"""plot the time of each tweet in a day
"""
tims = list()
plcs = list()
idm = id2int()
rows = dataset.loadrows(GEOTWEET, ('created_at',), ('place_id=\'{0}\''.format(place_id),), 'sample')
for line in rows:
if line['created_at'] == None:
continue
tim = time.strptime(str(line['created_at']), '%Y-%m-%d %H:%M:%S')
tims.append(tim.tm_wday + tim.tm_hour/24.0)
x = np.array(tims)
plt.hist(x, 42)
plt.title('Place {0}'.format(place_id))
plt.show()
def time_place_plot(user_id):
"""plot the time of each tweet in a day
"""
tims = list()
plcs = list()
idm = id2int()
rows = dataset.loadrows(GEOTWEET, ('created_at', 'place_id'), ('user_id={0}'.format(user_id),), 'sample')
for line in rows:
if line['created_at'] == None:
continue
tim = time.strptime(str(line['created_at']), '%Y-%m-%d %H:%M:%S')
plc = line['place_id']
tims.append(tim.tm_wday + tim.tm_hour/24.0)
plcs.append(idm.map(plc))
x = np.array(tims)
y = np.array(plcs)
plt.plot(x, y, 'o')
plt.title('User {0}'.format(user_id))
plt.show()
def top_poi_100_crs(dst, city, col):
"""Select all POIs from New York as the candidates"""
plst = dataset.loadrows(GEOTWEET, ('id',), \
('superior_name=\'{0}\''.format(city),), 'sample_dist_100')
twt_lst = dataset.load_by_place([plc['id'] for plc in plst])
places = dataset.DataItem()
vec_lst = dataset.Dataset()
for twt in twt_lst:
vec_lst.append(line2tf(comma_filter(fourq_filter(twt['text']))))
if twt['place_id'] not in places:
place = dataset.DataItem()
place['id'] = twt['place_id']
place['label'] = str(len(places))
place['name'] = twt['name']
place['category'] = twt['category']
place['super_category'] = twt['super_category']
places[twt['place_id']] = place
#output the places as json objects
with open(dst + '.place', 'w') as fplc:
for key in places:
print >> fplc, json.dumps(places[key])
#output the tweets as json objects
with open(dst + '.tweet', 'w') as ftwt:
i = 0
for twt in twt_lst:
print >> ftwt, json.dumps(twt)
#cut all the key that only appear less than 3 times
bgdist = vec_lst.bgdist()
keylist = list()
keylist.append('__NO__')
keylist.extend([key for key in bgdist.iterkeys() if bgdist[key] > 3])
keylist.append('__CLASS__')
for i in range(len(vec_lst)):
vec_lst[i]['__CLASS__'] = places[twt_lst[i]['place_id']][col]
vec_lst[i]['__NO__'] = i
vec_lst.gen_crs_arff(dst, 5, keylist)
def cate_smoothed_100(dst, city):
"""Select all POIs from New York as the candidates"""
plst = dataset.loadrows(GEOTWEET, ("id",), ("superior_name='{0}'".format(city),), "sample_dist_100")
twt_lst = dataset.load_by_place([plc["id"] for plc in plst])
twt_lst = smoothing.cate_smooth(twt_lst, 1, lambda x, y: True)
vec_lst = dataset.Dataset([line2tf(comma_filter(fourq_filter(twt["text"]))) for twt in twt_lst])
bgdist = vec_lst.bgdist()
# cut all the key that only appear less than 3 times
keylist = [key for key in bgdist.iterkeys() if bgdist[key] > 3]
keylist.append("__CLASS__")
for i in range(len(vec_lst)):
vec_lst[i]["__CLASS__"] = name_filter(dataset.place_name(twt_lst[i]["place_id"], GEOTWEET))
statistics.class_dist(vec)
vec_lst.gen_arff(dst, keylist)
def top_poi_100_crs(dst, city, col):
"""Select all POIs from New York as the candidates"""
plst = dataset.loadrows(GEOTWEET, ("id",), ("superior_name='{0}'".format(city),), "sample_dist_100")
twt_lst = dataset.load_by_place([plc["id"] for plc in plst])
places = dataset.DataItem()
vec_lst = dataset.Dataset()
for twt in twt_lst:
vec_lst.append(line2tf(comma_filter(fourq_filter(twt["text"]))))
if twt["place_id"] not in places:
place = dataset.DataItem()
place["id"] = twt["place_id"]
place["label"] = str(len(places))
place["name"] = twt["name"]
place["category"] = twt["category"]
place["super_category"] = twt["super_category"]
places[twt["place_id"]] = place
# output the places as json objects
with open(dst + ".place", "w") as fplc:
for key in places:
print >> fplc, json.dumps(places[key])
# output the tweets as json objects
with open(dst + ".tweet", "w") as ftwt:
i = 0
for twt in twt_lst:
print >> ftwt, json.dumps(twt)
# cut all the key that only appear less than 3 times
bgdist = vec_lst.bgdist()
keylist = list()
keylist.append("__NO__")
keylist.extend([key for key in bgdist.iterkeys() if bgdist[key] > 3])
keylist.append("__CLASS__")
for i in range(len(vec_lst)):
vec_lst[i]["__CLASS__"] = places[twt_lst[i]["place_id"]][col]
vec_lst[i]["__NO__"] = i
vec_lst.gen_crs_arff(dst, 5, keylist)
def cate_smoothed_100(dst, city):
"""Select all POIs from New York as the candidates"""
plst = dataset.loadrows(GEOTWEET, ('id',), \
('superior_name=\'{0}\''.format(city),), 'sample_dist_100')
twt_lst = dataset.load_by_place([plc['id'] for plc in plst])
twt_lst = smoothing.cate_smooth(twt_lst, 1, lambda x, y: True)
vec_lst = dataset.Dataset(
[line2tf(comma_filter(fourq_filter(twt['text']))) for twt in twt_lst])
bgdist = vec_lst.bgdist()
#cut all the key that only appear less than 3 times
keylist = [key for key in bgdist.iterkeys() if bgdist[key] > 3]
keylist.append('__CLASS__')
for i in range(len(vec_lst)):
vec_lst[i]['__CLASS__'] = name_filter(
dataset.place_name(twt_lst[i]['place_id'], GEOTWEET))
statistics.class_dist(vec)
vec_lst.gen_arff(dst, keylist)
def all_poi_100(dst, col):
"""Select all POIs from New York as the candidates"""
plst = dataset.loadrows(GEOTWEET, ("id",), None, "sample_dist_100")
twt_lst = dataset.load_by_place([plc["id"] for plc in plst])
places = dataset.DataItem()
vec_lst = dataset.Dataset()
for twt in twt_lst:
vec_lst.append(line2tf(comma_filter(fourq_filter(twt["text"]))))
if twt["place_id"] not in places:
place = dataset.DataItem()
place["id"] = twt["place_id"]
place["label"] = str(len(places))
place["name"] = twt["name"]
place["category"] = twt["category"]
place["super_category"] = twt["super_category"]
places[twt["place_id"]] = place
# output the places as json objects
with open(dst + ".place", "w") as fplc:
for key in places:
print >> fplc, json.dumps(places[key])
# output the tweets as json objects
with open(dst + ".tweet", "w") as ftwt:
i = 0
for twt in twt_lst:
print >> ftwt, json.dumps(twt)
# cut all the key that only appear less than 3 times
bgdist = vec_lst.bgdist()
keylist = list()
keylist.append("__NO__")
keylist.extend([key for key in bgdist.iterkeys() if bgdist[key] > 3])
keylist.append("__CLASS__")
# add idf divisor
# idf = vec_lst.idf()
# for vec in vec_lst:
# for key in vec.iterkeys():
# vec[key] = vec[key]/math.log(float(idf[key])+1)
for i in range(len(vec_lst)):
vec_lst[i]["__CLASS__"] = name_filter(places[twt_lst[i]["place_id"]][col])
vec_lst[i]["__NO__"] = i
# def wdist(vec_lst):
# """get the back ground distribution"""
# bgdist = dict()
# for vec in vec_lst:
# for key in vec.iterkeys():
# if key in bgdist:
# bgdist[key] += vec[key]
# else:
# bgdist[key] = vec[key]
# return bgdist
# wpdist = vec_lst.groupfunc('__CLASS__', wdist)
# for key in wpdist.iterkeys():
# print (sorted(wpdist[key], key=itemgetter(1), reverse=True))[0:10]
vec_lst.gen_arff(dst, keylist)
def all_poi_100(dst, col):
"""Select all POIs from New York as the candidates"""
plst = dataset.loadrows(GEOTWEET, ('id',), \
None, 'sample_dist_100')
twt_lst = dataset.load_by_place([plc['id'] for plc in plst])
places = dataset.DataItem()
vec_lst = dataset.Dataset()
for twt in twt_lst:
vec_lst.append(line2tf(comma_filter(fourq_filter(twt['text']))))
if twt['place_id'] not in places:
place = dataset.DataItem()
place['id'] = twt['place_id']
place['label'] = str(len(places))
place['name'] = twt['name']
place['category'] = twt['category']
place['super_category'] = twt['super_category']
places[twt['place_id']] = place
#output the places as json objects
with open(dst + '.place', 'w') as fplc:
for key in places:
print >> fplc, json.dumps(places[key])
#output the tweets as json objects
with open(dst + '.tweet', 'w') as ftwt:
i = 0
for twt in twt_lst:
print >> ftwt, json.dumps(twt)
#cut all the key that only appear less than 3 times
bgdist = vec_lst.bgdist()
keylist = list()
keylist.append('__NO__')
keylist.extend([key for key in bgdist.iterkeys() if bgdist[key] > 3])
keylist.append('__CLASS__')
# add idf divisor
#idf = vec_lst.idf()
#for vec in vec_lst:
#for key in vec.iterkeys():
#vec[key] = vec[key]/math.log(float(idf[key])+1)
for i in range(len(vec_lst)):
vec_lst[i]['__CLASS__'] = name_filter(
places[twt_lst[i]['place_id']][col])
vec_lst[i]['__NO__'] = i
#def wdist(vec_lst):
#"""get the back ground distribution"""
#bgdist = dict()
#for vec in vec_lst:
#for key in vec.iterkeys():
#if key in bgdist:
#bgdist[key] += vec[key]
#else:
#bgdist[key] = vec[key]
#return bgdist
#wpdist = vec_lst.groupfunc('__CLASS__', wdist)
#for key in wpdist.iterkeys():
#print (sorted(wpdist[key], key=itemgetter(1), reverse=True))[0:10]
vec_lst.gen_arff(dst, keylist)
tid_set = set(twt_lst.distinct('place_id'))
pid_set = set(pid_lst)
for pid in pid_lst:
plc = dataset.loadrows(GEOTWEET, ('id', 'lat', 'lng', 'super_category'), \
('id = \'{0}\''.format(pid),), 'place')
plc_type = plc[0]['super_category']
tmp_lst = list()
cand = dataset.type_random(plc_type)
for twt in cand:
if twt['id'] not in tid_set and twt['place_id'] not in pid_lst:
if sel(twt, plc):
twt['place_id'] = pid
tid_set.add(twt['id'])
pid_set.add(twt['place_id'])
tmp_lst.append(twt)
if len(tmp_lst) >= ratio * twt_dist[pid]: break
rst_lst.extend(tmp_lst)
rst_lst.extend(twt_lst)
return rst_lst
if __name__ == '__main__':
twt_lst = cate_smooth(dataset.loadrows(GEOTWEET, ('text', 'place_id'), ('place_id = \'0002ac59702e20cf\'',)), 10, lambda x, y: True)
print '----------------------'
for twt in twt_lst:
print twt
for pid in pid_lst:
plc = dataset.loadrows(GEOTWEET, ('id', 'lat', 'lng', 'super_category'), \
('id = \'{0}\''.format(pid),), 'place')
plc_type = plc[0]['super_category']
tmp_lst = list()
cand = dataset.type_random(plc_type)
for twt in cand:
if twt['id'] not in tid_set and twt['place_id'] not in pid_lst:
if sel(twt, plc):
twt['place_id'] = pid
tid_set.add(twt['id'])
pid_set.add(twt['place_id'])
tmp_lst.append(twt)
if len(tmp_lst) >= ratio * twt_dist[pid]: break
rst_lst.extend(tmp_lst)
rst_lst.extend(twt_lst)
return rst_lst
if __name__ == '__main__':
twt_lst = cate_smooth(
dataset.loadrows(GEOTWEET, ('text', 'place_id'),
('place_id = \'0002ac59702e20cf\'', )), 10,
lambda x, y: True)
print '----------------------'
for twt in twt_lst:
print twt
def cnt_poi(city, table='sample'):
""" Draw the tweet distribution over POIs."""
twt_lst = dataset.loadrows(GEOTWEET, ('place_id', 'count(id) as cnt'),
"superior_id='%s'" % (city), table, 'group by place_id')
def top_poi100_map():
plc_lst = dataset.loadrows(GEOTWEET, ('lat', 'lng', 'name'), ('superior_name=\'los angeles\'',), 'sample_dist_100')
for plc in plc_lst:
plc['text'] = plc['name']
geo_map('../la_100.html', plc_lst)