def __init__(self, region, start_timestamp, end_timestamp, freq="1h"):
# super(InstagramTimeSeries, self).__init__(region,start_timestamp, end_timestamp, freq)
self.start_timestamp = start_timestamp
self.end_timestamp = end_timestamp
self.region = region
self._db = MongoDBInterface()
self._db.setDB(InstagramConfig.db)
self._db.setCollection(InstagramConfig.posts_collection)
self.days_to_predict = 1
self.freq = freq
class InstagramTimeSeries:
def __init__(self, region, start_timestamp, end_timestamp, freq="1h"):
# super(InstagramTimeSeries, self).__init__(region,start_timestamp, end_timestamp, freq)
self.start_timestamp = start_timestamp
self.end_timestamp = end_timestamp
self.region = region
self._db = MongoDBInterface()
self._db.setDB(InstagramConfig.db)
self._db.setCollection(InstagramConfig.posts_collection)
self.days_to_predict = 1
self.freq = freq
def rangeQuery(self, region, startTimestamp, endTimestamp):
region_conditions = {}
period_conditions = {}
if region:
region_conditions = {"region.code": region}
period_conditions = {"created_time": {"$gte": startTimestamp, "$lt": endTimestamp}}
conditions = dict(region_conditions, **period_conditions)
return self._db.getAllDocuments(conditions).sort([("created_time", -1)])
def getRawSeries(self):
return self.series
def buildTimeSeries(self, count_people=True, avoid_flooding=True):
"""Return a pandas Series object
count_people = True means we only want to count single user
instead of # of photos for that region
avoid_flooding = True means we want to avoid a single user
flooding many photos into instagram in a short time. Now we
set the time window as within 5 minutes only count as a single
user
"""
window_avoid_flooding = 300
data = []
photo_cnt = 0
for photo in self.rangeQuery(self.region, self.start_timestamp, self.end_timestamp):
p = {"user": photo["user"], "created_time": photo["created_time"]}
data.append(p)
photo_cnt += 1
if photo_cnt % 10000 == 0:
print photo_cnt
data = sorted(data, key=lambda x: x["created_time"])
print (len(data))
user_last_upload = {} # for a single user, when is his last upload
counts = []
dates = []
counts.append(1) # VERY IMPORTANT. FIX THE SIZE OF TIMESERIES IN PANDAS
dates.append(datetime.utcfromtimestamp(float(self.start_timestamp)))
for photo_json in data:
user = photo_json["user"]["username"]
utc_date = datetime.utcfromtimestamp(float(photo_json["created_time"]))
if count_people:
if user not in user_last_upload:
user_last_upload[user] = int(photo_json["created_time"])
dates.append(utc_date)
counts.append(1)
else:
if float(photo_json["created_time"]) - float(user_last_upload[user]) > window_avoid_flooding:
user_last_upload[user] = int(photo_json["created_time"])
dates.append(utc_date)
counts.append(1)
else:
dates.append(utc_date)
counts.append(1)
counts.append(1) # VERY IMPORTANT, FIX THE SIZE OF TIMESERIES IN PANDAS
dates.append(datetime.utcfromtimestamp(float(self.end_timestamp) - 1))
self.series = Series(counts, index=dates)
print (self.series.count())
try:
self.series2 = self.series.resample(self.freq, how="sum", label="right")
# self.series2 = self.series2.fillna(0) #fill NaN values with zeros
except Exception as e: # not enough data
print (e)
pass
print (self.series2.count())
return self.series2
def smoothSeriesEwma(self, series, span=5.0, adjust=True, halflife=None, min_periods=0, how="mean"):
return pandas.ewma(
series,
com=None,
span=span,
halflife=halflife,
min_periods=min_periods,
freq="1h",
adjust=adjust,
how=how,
ignore_na=True,
)
def smoothSeriesEwmstd(self, series, span=5.0, adjust=True, halflife=None, min_periods=0):
return pandas.ewmstd(
series, com=None, span=span, halflife=halflife, min_periods=min_periods, adjust=adjust, ignore_na=True
)
def smoothSeriesEwmvar(self, series, span=5.0, adjust=True, halflife=None, min_periods=0):
return pandas.ewmstd(
series, com=None, span=span, halflife=halflife, min_periods=min_periods, adjust=adjust, ignore_na=True
)
def dataPrepare(self, serie):
"""This is to return the 'future data points' that you want to
predict. e.g. predict for each hour tomorrow how many people will
show up at Times Square
"""
ts = serie
index = ts.index
if len(index) < 3:
raise Exception("Only %d data points" % (len(index)))
start_date = ts.index[0]
"""Notice training here is in the format of
(days from begining of the timeseries, number of data at that time)
"""
training = []
for idx in index:
days_diff = (idx - start_date).days + (idx - start_date).seconds / (24 * 3600.0)
training.append((days_diff, ts[idx]))
nearest_current_date = index[-1]
testing = []
align = []
converted_align = []
for hour in range(25 * self.days_to_predict):
next_date = nearest_current_date + timedelta(seconds=3600 * (hour + 1))
delta = next_date - start_date
days_from_start = (delta.seconds + delta.days * 86400) / (3600 * 24.0)
testing.append(days_from_start)
align.append(next_date)
converted_align.append(calendar.timegm(next_date.utctimetuple()))
return training, testing, align, converted_align
