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90cfcaaecc
Ok, now I'm getting some angst in my commit messages like my predecessors had. I understand now. It's a terrible burden. Why must the calendar progress? Why must numbers increment? The world is forever turning. The future is here. It is 2014.
270 lines
9.2 KiB
Python
270 lines
9.2 KiB
Python
#!/usr/bin/python
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# The contents of this file are subject to the Common Public Attribution
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# License Version 1.0. (the "License"); you may not use this file except in
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# compliance with the License. You may obtain a copy of the License at
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# http://code.reddit.com/LICENSE. The License is based on the Mozilla Public
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# License Version 1.1, but Sections 14 and 15 have been added to cover use of
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# software over a computer network and provide for limited attribution for the
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# Original Developer. In addition, Exhibit A has been modified to be consistent
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# with Exhibit B.
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#
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# Software distributed under the License is distributed on an "AS IS" basis,
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# WITHOUT WARRANTY OF ANY KIND, either express or implied. See the License for
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# the specific language governing rights and limitations under the License.
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#
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# The Original Code is reddit.
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#
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# The Original Developer is the Initial Developer. The Initial Developer of
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# the Original Code is reddit Inc.
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#
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# All portions of the code written by reddit are Copyright (c) 2006-2014 reddit
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# Inc. All Rights Reserved.
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###############################################################################
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"""Tools for evaluating promoted link distribution."""
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from collections import defaultdict
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import datetime
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from math import sqrt
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from pylons import g
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from sqlalchemy.sql.functions import sum as sa_sum
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from r2.lib import promote
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from r2.lib.db.operators import and_, or_
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from r2.lib.utils import to36, weighted_lottery
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from r2.models.traffic import (
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Session,
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TargetedImpressionsByCodename,
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PageviewsBySubredditAndPath,
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)
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from r2.models.bidding import PromotionWeights
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from r2.models import (
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Link,
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PromoCampaign,
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DefaultSR,
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)
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LINK_PREFIX = Link._type_prefix + str(Link._type_id)
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PC_PREFIX = PromoCampaign._type_prefix + str(PromoCampaign._type_id)
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def error_statistics(errors):
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mean_error = sum(errors) / len(errors)
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min_error = min([abs(i) for i in errors])
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max_error = max([abs(i) for i in errors])
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stdev_error = sqrt(
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(sum([i ** 2 for i in errors]) / len(errors))
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- mean_error ** 2)
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return (mean_error, min_error, max_error, stdev_error)
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def get_scheduled(date, sr_name=''):
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all_promotions = PromotionWeights.get_campaigns(date)
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fp_promotions = [p for p in all_promotions if p.sr_name == sr_name]
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campaigns = PromoCampaign._byID([i.promo_idx for i in fp_promotions],
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return_dict=False, data=True)
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links = Link._by_fullname([i.thing_name for i in fp_promotions],
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return_dict=False, data=True)
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links = {l._id: l for l in links}
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kept = []
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for camp in campaigns:
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if camp.trans_id == 0:
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continue
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link = links[camp.link_id]
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if link._spam or not promote.is_accepted(link):
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continue
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kept.append(camp._id)
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return [('%s_%s' % (PC_PREFIX, to36(p.promo_idx)), p.thing_name, p.bid)
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for p in fp_promotions if p.promo_idx in kept]
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def get_campaign_pageviews(date, sr_name=''):
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# ads go live at hour=5
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start = datetime.datetime(date.year, date.month, date.day, 5, 0)
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hours = [start + datetime.timedelta(hours=i) for i in xrange(24)]
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traffic_cls = TargetedImpressionsByCodename
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codename_string = PC_PREFIX + '_%'
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q = (Session.query(traffic_cls.codename,
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sa_sum(traffic_cls.pageview_count).label('daily'))
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.filter(traffic_cls.subreddit == sr_name)
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.filter(traffic_cls.codename.like(codename_string))
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.filter(traffic_cls.interval == 'hour')
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.filter(traffic_cls.date.in_(hours))
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.group_by(traffic_cls.codename))
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pageviews = dict(q)
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return pageviews
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def filter_campaigns(date, fullnames):
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campaigns = PromoCampaign._by_fullname(fullnames, data=True,
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return_dict=False)
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# filter out campaigns that shouldn't be live
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pc_date = datetime.datetime(date.year, date.month, date.day, 0, 0,
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tzinfo=g.tz)
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campaigns = [camp for camp in campaigns
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if camp.start_date <= pc_date <= camp.end_date]
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# check for links with targeted campaigns - we can't handle them now
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has_targeted = [camp.link_id for camp in campaigns if camp.sr_name != '']
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return [camp for camp in campaigns if camp.link_id not in has_targeted]
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def get_frontpage_pageviews(date):
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sr_name = DefaultSR.name
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traffic_cls = PageviewsBySubredditAndPath
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q = (Session.query(traffic_cls.srpath, traffic_cls.pageview_count)
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.filter(traffic_cls.interval == 'day')
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.filter(traffic_cls.date == date)
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.filter(traffic_cls.srpath == '%s-GET_listing' % sr_name))
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r = list(q)
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return r[0][1]
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def compare_pageviews(daysago=0, verbose=False):
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"""Evaluate past delivery for promoted links.
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Check frontpage promoted links for their actual delivery compared to what
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would be expected based on their bids.
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"""
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date = (datetime.datetime.now(g.tz) -
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datetime.timedelta(days=daysago)).date()
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scheduled = get_scheduled(date)
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pageviews_by_camp = get_campaign_pageviews(date)
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campaigns = filter_campaigns(date, pageviews_by_camp.keys())
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actual = []
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for camp in campaigns:
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link_fullname = '%s_%s' % (LINK_PREFIX, to36(camp.link_id))
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i = (camp._fullname, link_fullname, pageviews_by_camp[camp._fullname])
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actual.append(i)
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scheduled_links = {link for camp, link, pageviews in scheduled}
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actual_links = {link for camp, link, pageviews in actual}
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bid_by_link = defaultdict(int)
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total_bid = 0
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pageviews_by_link = defaultdict(int)
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total_pageviews = 0
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for camp, link, bid in scheduled:
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if link not in actual_links:
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if verbose:
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print '%s not found in actual, skipping' % link
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continue
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bid_by_link[link] += bid
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total_bid += bid
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for camp, link, pageviews in actual:
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# not ideal: links shouldn't be here
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if link not in scheduled_links:
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if verbose:
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print '%s not found in schedule, skipping' % link
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continue
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pageviews_by_link[link] += pageviews
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total_pageviews += pageviews
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errors = []
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for link, bid in sorted(bid_by_link.items(), key=lambda t: t[1]):
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pageviews = pageviews_by_link.get(link, 0)
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expected = bid / total_bid
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realized = float(pageviews) / total_pageviews
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difference = (realized - expected) / expected
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errors.append(difference)
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if verbose:
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print '%s - %s - %s - %s' % (link, expected, realized, difference)
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mean_error, min_error, max_error, stdev_error = error_statistics(errors)
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print '%s' % date
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print ('error %s max, %s min, %s +- %s' %
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(max_error, min_error, mean_error, stdev_error))
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print 'total bid %s' % total_bid
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print ('pageviews for promoted links targeted only to frontpage %s' %
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total_pageviews)
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print ('frontpage pageviews for all promoted links %s' %
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sum(pageviews_by_camp.values()))
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print 'promoted eligible pageviews %s' % get_frontpage_pageviews(date)
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PROMOS = [('promo_%s' % i, i + 1) for i in xrange(100)]
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def select_subset(n, weighted=False):
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promos = copy(PROMOS)
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selected = []
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if weighted:
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d = {(name, weight): weight for name, weight in promos}
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while len(selected) < n and d:
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i = weighted_lottery(d)
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del d[i]
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selected.append(i)
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else:
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# Sample without replacement
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if n > len(promos):
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return promos
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else:
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return random.sample(promos, n)
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return selected
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def pick(subset, weighted=False):
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if weighted:
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d = {(name, weight): weight for name, weight in subset}
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picked = weighted_lottery(d)
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else:
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picked = random.choice(subset)
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return picked
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def benchmark(subsets=1440, picks=6945, weighted_subset=False,
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weighted_pick=True, subset_size=10, verbose=False):
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"""Test 2 stage randomization.
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First stage picks a subset of promoted links, second stage picks a single
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promoted link. This is to simulate the server side subset plus client side
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randomization of promoted link display.
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"""
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counts = {(name, weight): 0 for name, weight in PROMOS}
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for i in xrange(subsets):
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subset = select_subset(subset_size, weighted=weighted_subset)
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for j in xrange(picks):
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name, weight = pick(subset, weighted=weighted_pick)
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counts[(name, weight)] += 1
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total_weight = sum(counts.values())
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errors = []
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for name, weight in sorted(counts.keys(), key=lambda t: t[1]):
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count = counts[(name, weight)]
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actual = float(count) / (subsets * picks)
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expected = float(weight) / total_weight
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error = (actual - expected) / expected
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errors.append(error)
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if verbose:
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print ('%s - expected: %s - actual: %s - error %s' %
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(name, expected, actual, error))
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mean_error, min_error, max_error, stdev_error = error_statistics(errors)
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if verbose:
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print ('Error %s max, %s min, %s +- %s' %
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(max_error, min_error, mean_error, stdev_error))
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return (max_error, min_error, mean_error, stdev_error)
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