def update_prediction_data():
"""Fetch prediction data and write it to cassandra."""
min_daily_by_sr = _min_daily_pageviews_by_sr(NDAYS_TO_QUERY)
# combine front page values (sometimes frontpage gets '' for its name)
if '' in min_daily_by_sr:
fp = DefaultSR.name.lower()
min_daily_by_sr[fp] = min_daily_by_sr.get(fp, 0) + min_daily_by_sr['']
del min_daily_by_sr['']
filtered = {sr_name: num for sr_name, num in min_daily_by_sr.iteritems()
if num > 100}
PromoMetrics.set(MIN_DAILY_CASS_KEY, filtered)
def get_predicted_pageviews(srs, start, end):
srs, is_single = tup(srs, ret_is_single=True)
sr_names = [sr.name for sr in srs]
# default subreddits require a different inventory factor
content_langs = [g.site_lang]
default_srids = Subreddit.top_lang_srs(content_langs,
limit=g.num_default_reddits,
filter_allow_top=True,
over18=False,
ids=True)
# prediction does not vary by date
daily_inventory = PromoMetrics.get(MIN_DAILY_CASS_KEY, sr_names=sr_names)
dates = get_date_range(start, end)
ret = {}
for sr in srs:
if not isinstance(sr, FakeSubreddit) and sr._id in default_srids:
factor = DEFAULT_INVENTORY_FACTOR
else:
factor = INVENTORY_FACTOR
sr_daily_inventory = daily_inventory.get(sr.name, 0) * factor
sr_daily_inventory = int(sr_daily_inventory)
ret[sr.name] = dict.fromkeys(dates, sr_daily_inventory)
if is_single:
return ret[srs[0].name]
else:
return ret
def get_predicted_pageviews(srs, start, end):
srs, is_single = tup(srs, ret_is_single=True)
sr_names = [sr.name for sr in srs]
# default subreddits require a different inventory factor
content_langs = [g.site_lang]
default_srids = Subreddit.top_lang_srs(content_langs,
limit=g.num_default_reddits,
filter_allow_top=True, over18=False,
ids=True)
# prediction does not vary by date
daily_inventory = PromoMetrics.get(MIN_DAILY_CASS_KEY, sr_names=sr_names)
dates = get_date_range(start, end)
ret = {}
for sr in srs:
if not isinstance(sr, FakeSubreddit) and sr._id in default_srids:
factor = DEFAULT_INVENTORY_FACTOR
else:
factor = INVENTORY_FACTOR
sr_daily_inventory = daily_inventory.get(sr.name, 0) * factor
sr_daily_inventory = int(sr_daily_inventory)
ret[sr.name] = dict.fromkeys(dates, sr_daily_inventory)
if is_single:
return ret[srs[0].name]
else:
return ret
def get_predicted_by_date(sr_name, start, stop=None):
"""Return dict mapping datetime objects to predicted pageviews."""
# lowest pageviews any day the last 2 weeks
min_daily = PromoMetrics.get(MIN_DAILY_CASS_KEY, sr_name).get(sr_name, 0)
# expand out to the requested range of dates
ndays = (stop - start).days if stop else 1 # default is one day
predicted = OrderedDict()
for i in range(ndays):
date = start + timedelta(i)
predicted[date] = min_daily
return predicted
def get_predicted_by_date(sr_name, start, stop=None):
"""Return dict mapping datetime objects to predicted pageviews."""
if not sr_name:
sr_name = DefaultSR.name.lower()
# lowest pageviews any day the last 2 weeks
min_daily = PromoMetrics.get(MIN_DAILY_CASS_KEY, sr_name).get(sr_name, 0)
# expand out to the requested range of dates
ndays = (stop - start).days if stop else 1 # default is one day
predicted = OrderedDict()
for i in range(ndays):
date = start + timedelta(i)
predicted[date] = min_daily
return predicted
def get_predicted_by_date(sr_name, start, stop=None):
"""
For now, use lowest pageviews in the subreddit any day the last two weeks
as a simple heuristic.
"""
# lowest pageviews any day the last 2 weeks
min_daily = PromoMetrics.get(CassKeys.MIN_DAILY, sr_name).get(sr_name, 0)
# expand out to the requested range of dates
ndays = (stop - start).days if stop else 1 # default is one day
predicted = OrderedDict()
for i in range(ndays):
date = start + timedelta(i)
predicted[date] = min_daily
return predicted
def get_predicted_by_date(sr_name, start, stop=None):
'''
For now, use lowest pageviews in the subreddit any day the last two weeks
as a simple heuristic.
'''
# lowest pageviews any day the last 2 weeks
min_daily = PromoMetrics.get(CassKeys.MIN_DAILY, sr_name).get(sr_name, 0)
# expand out to the requested range of dates
ndays = (stop - start).days if stop else 1 # default is one day
predicted = OrderedDict()
for i in range(ndays):
date = start + timedelta(i)
predicted[date] = min_daily
return predicted
def get_predicted_pageviews(srs, start, end):
srs, is_single = tup(srs, ret_is_single=True)
sr_names = [sr.name for sr in srs]
# prediction does not vary by date
daily_inventory = PromoMetrics.get(MIN_DAILY_CASS_KEY, sr_names=sr_names)
dates = get_date_range(start, end)
ret = {}
for sr in srs:
sr_daily_inventory = daily_inventory.get(sr.name, 0) * INVENTORY_FACTOR
sr_daily_inventory = int(sr_daily_inventory)
ret[sr.name] = dict.fromkeys(dates, sr_daily_inventory)
if is_single:
return ret[srs[0].name]
else:
return ret
def get_predicted_pageviews(srs, start, end):
srs, is_single = tup(srs, ret_is_single=True)
sr_names = [sr.name for sr in srs]
# prediction does not vary by date
daily_inventory = PromoMetrics.get(MIN_DAILY_CASS_KEY, sr_names=sr_names)
dates = get_date_range(start, end)
ret = {}
for sr in srs:
sr_daily_inventory = daily_inventory.get(sr.name, 0) * INVENTORY_FACTOR
sr_daily_inventory = int(sr_daily_inventory)
ret[sr.name] = dict.fromkeys(dates, sr_daily_inventory)
if is_single:
return ret[srs[0].name]
else:
return ret
def get_predicted_pageviews(srs, location=None):
"""
Return predicted number of pageviews for sponsored headlines.
Predicted geotargeted impressions are estimated as:
geotargeted impressions = (predicted untargeted impressions) *
(fp impressions for location / fp impressions)
"""
srs, is_single = tup(srs, ret_is_single=True)
sr_names = [sr.name for sr in srs]
# default subreddits require a different inventory factor
default_srids = LocalizedDefaultSubreddits.get_global_defaults()
if location:
no_location = Location(None)
r = LocationPromoMetrics.get(DefaultSR, [no_location, location])
location_pageviews = r[(DefaultSR, location)]
all_pageviews = r[(DefaultSR, no_location)]
if all_pageviews:
location_factor = float(location_pageviews) / float(all_pageviews)
else:
location_factor = 0.
else:
location_factor = 1.0
# prediction does not vary by date
daily_inventory = PromoMetrics.get(MIN_DAILY_CASS_KEY, sr_names=sr_names)
ret = {}
for sr in srs:
if not isinstance(sr, FakeSubreddit) and sr._id in default_srids:
default_factor = DEFAULT_INVENTORY_FACTOR
else:
default_factor = INVENTORY_FACTOR
base_pageviews = daily_inventory.get(sr.name, 0)
ret[sr.name] = int(base_pageviews * default_factor * location_factor)
if is_single:
return ret[srs[0].name]
else:
return ret
def get_predicted_pageviews(srs, location=None):
"""
Return predicted number of pageviews for sponsored headlines.
Predicted geotargeted impressions are estimated as:
geotargeted impressions = (predicted untargeted impressions) *
(fp impressions for location / fp impressions)
"""
srs, is_single = tup(srs, ret_is_single=True)
sr_names = [sr.name for sr in srs]
# default subreddits require a different inventory factor
default_srids = LocalizedDefaultSubreddits.get_global_defaults()
if location:
no_location = Location(None)
r = LocationPromoMetrics.get(DefaultSR, [no_location, location])
location_pageviews = r[(DefaultSR, location)]
all_pageviews = r[(DefaultSR, no_location)]
if all_pageviews:
location_factor = float(location_pageviews) / float(all_pageviews)
else:
location_factor = 0.
else:
location_factor = 1.0
# prediction does not vary by date
daily_inventory = PromoMetrics.get(MIN_DAILY_CASS_KEY, sr_names=sr_names)
ret = {}
for sr in srs:
if not isinstance(sr, FakeSubreddit) and sr._id in default_srids:
default_factor = DEFAULT_INVENTORY_FACTOR
else:
default_factor = INVENTORY_FACTOR
base_pageviews = daily_inventory.get(sr.name, 0)
ret[sr.name] = int(base_pageviews * default_factor * location_factor)
if is_single:
return ret[srs[0].name]
else:
return ret
def get_predicted_pageviews(srs):
srs, is_single = tup(srs, ret_is_single=True)
sr_names = [sr.name for sr in srs]
# default subreddits require a different inventory factor
default_srids = LocalizedDefaultSubreddits.get_global_defaults()
# prediction does not vary by date
daily_inventory = PromoMetrics.get(MIN_DAILY_CASS_KEY, sr_names=sr_names)
ret = {}
for sr in srs:
if not isinstance(sr, FakeSubreddit) and sr._id in default_srids:
factor = DEFAULT_INVENTORY_FACTOR
else:
factor = INVENTORY_FACTOR
ret[sr.name] = int(daily_inventory.get(sr.name, 0) * factor)
if is_single:
return ret[srs[0].name]
else:
return ret
def get_available_pageviews(srs, start, end, datestr=False, ignore=None):
srs, is_single = tup(srs, ret_is_single=True)
sr_names = [sr.name for sr in srs]
daily_inventory = PromoMetrics.get(MIN_DAILY_CASS_KEY, sr_names=sr_names)
sold_by_sr_by_date = get_sold_pageviews(srs, start, end, ignore)
datekey = lambda dt: dt.strftime('%m/%d/%Y') if datestr else dt
ret = {}
for sr in srs:
sold_by_date = sold_by_sr_by_date[sr.name]
sr_ad_inventory = int(daily_inventory.get(sr.name, 0) * 1.00)
ret[sr.name] = {
datekey(date): max(0, sr_ad_inventory - sold)
for date, sold in sold_by_date.iteritems()
}
if is_single:
return ret[srs[0].name]
else:
return ret
def get_available_pageviews(srs, start, end, datestr=False,
ignore=None):
srs, is_single = tup(srs, ret_is_single=True)
sr_names = [sr.name for sr in srs]
daily_inventory = PromoMetrics.get(MIN_DAILY_CASS_KEY, sr_names=sr_names)
sold_by_sr_by_date = get_sold_pageviews(srs, start, end, ignore)
datekey = lambda dt: dt.strftime('%m/%d/%Y') if datestr else dt
ret = {}
for sr in srs:
sold_by_date = sold_by_sr_by_date[sr.name]
sr_ad_inventory = int(daily_inventory.get(sr.name, 0) * 1.00)
ret[sr.name] = {
datekey(date): max(0, sr_ad_inventory - sold)
for date, sold in sold_by_date.iteritems()
}
if is_single:
return ret[srs[0].name]
else:
return ret
def update_prediction_data():
"""
Fetches prediction data and writes it to cassandra.
"""
min_daily_by_sr = _min_daily_pageviews_by_sr(NDAYS_TO_QUERY)
PromoMetrics.set(CassKeys.MIN_DAILY, min_daily_by_sr)
def update_prediction_data():
"""Fetch prediction data and write it to cassandra."""
min_daily_by_sr = _min_daily_pageviews_by_sr(NDAYS_TO_QUERY)
PromoMetrics.set(MIN_DAILY_CASS_KEY, min_daily_by_sr)
def update_prediction_data():
"""Fetch prediction data and write it to cassandra."""
min_daily_by_sr = _min_daily_pageviews_by_sr(NDAYS_TO_QUERY)
PromoMetrics.set(MIN_DAILY_CASS_KEY, min_daily_by_sr)
def update_prediction_data():
'''
Fetches prediction data and writes it to cassandra.
'''
min_daily_by_sr = _min_daily_pageviews_by_sr(NDAYS_TO_QUERY)
PromoMetrics.set(CassKeys.MIN_DAILY, min_daily_by_sr)
