def full_train():
combo = sframes.load('combo.gl')
extras = sframes.load('extras.gl')
combo.add_columms(extras)
prepareSFrame(combo)
cit = chunk_iterator(combo)
sit = select_train(cit, True)
model = train(sit)
return model
def full_train():
combo = sframes.load('combo.gl')
extras = sframes.load('extras.gl')
combo.add_columms(extras)
prepareSFrame(combo)
cit = chunk_iterator(combo)
sit = select_train(cit, True)
model = train(sit)
return model
def get_context_ads():
'''
Loads the ads.gl file (all of AdsInfo.tsv) and extracts the context ads.
'''
ads = sframes.load('ads.gl')
ctx = ads[ads['IsContext']]
del ctx['IsContext']
del ctx['LocationID']
return ctx
def basic_join(tss, si, user):
'''
A generator that performs a rolling join over Graphlab SFrames tss, which
stores data from train/testSearchStream.tsv and si, which is from
SearchInfo.tsv. SFrame context_ads.gl, which has the contextual ads from
AdsInfo.tsv, is loaded and joined in. UserInfo.tsv is joined in from loading
the artifact user_dict.pkl from artifacts/.
args:
tss - an SFrame with data from trainSearchStream or testSearchStream,
including samples or validation sets
si - an SFrame with data from SearchInfo. Must have all of the SearchIDs
in tss, but it can be a sample
user - dict or None. A dict from UserID to a dict of features for
that user. Caller should construct this if used.
generates:
a dict that combines all of the fields from tss, si and ads for a row
'''
ctx = sframes.load('context_ads.gl')
ctx = sframes.sframe_to_dict('AdID', ctx)
si_it = iter(si)
si_line = si_it.next()
for tss_line in tss:
search_id = tss_line['SearchID']
ad_id = tss_line['AdID']
user_id = si_line['UserID']
while search_id != si_line['SearchID']:
si_line = si_it.next()
# Now the SearchIDs match
tss_line.update(ctx[ad_id])
# SearchInfo.CategoryID overwrites AdInfo.CategoryID in this line
tss_line.update(si_line)
if user is not None and user_id in user:
tss_line.update(user[user_id])
yield tss_line
def basic_join(tss, si, user):
'''
A generator that performs a rolling join over Graphlab SFrames tss, which
stores data from train/testSearchStream.tsv and si, which is from
SearchInfo.tsv. SFrame context_ads.gl, which has the contextual ads from
AdsInfo.tsv, is loaded and joined in. UserInfo.tsv is joined in from loading
the artifact user_dict.pkl from artifacts/.
args:
tss - an SFrame with data from trainSearchStream or testSearchStream,
including samples or validation sets
si - an SFrame with data from SearchInfo. Must have all of the SearchIDs
in tss, but it can be a sample
user - dict or None. A dict from UserID to a dict of features for
that user. Caller should construct this if used.
generates:
a dict that combines all of the fields from tss, si and ads for a row
'''
ctx = sframes.load('context_ads.gl')
ctx = sframes.sframe_to_dict('AdID', ctx)
si_it = iter(si)
si_line = si_it.next()
for tss_line in tss:
search_id = tss_line['SearchID']
ad_id = tss_line['AdID']
user_id = si_line['UserID']
while search_id != si_line['SearchID']:
si_line = si_it.next()
# Now the SearchIDs match
tss_line.update(ctx[ad_id])
# SearchInfo.CategoryID overwrites AdInfo.CategoryID in this line
tss_line.update(si_line)
if user is not None and user_id in user:
tss_line.update(user[user_id])
yield tss_line
help='# of passes over training data.')
args = parser.parse_args()
if args.users=='full':
users = build_user_dict()
print 'loading full user data'
elif args.users=='counts':
users = avito2_io.get_artifact('user_counts.pkl')
print 'loading user counts only from user_counts.pkl'
elif args.users == 'si':
users = avito2_io.get_artifact('user_si.pkl')
print 'loading user dict from user_si.pkl'
else:
users = None
D = 2**args.bits
if args.all:
tr = sframes.load('train_context.gl')
si = sframes.load('search.gl')
if not args.sub:
raise Warning('--all without --sub is not sensible.')
else:
tr = sframes.load('train_ds.gl')
si = sframes.load('search_ds.gl')
# no interactions; it'd take days
model = train(tr,
si,
args.alpha,
args.beta,
args.l1,
args.l2,
D,
users,
def features2():
'''
This function implements and records the construction of the second set of
graphlab sframe features. These include all of the integer-valued raw and
lightly processed features from train/test, SearchInfo, Category, Location,
AdsInfo and UserInfo. Only contextual ads are considered.
NB: This leaves SearchID in the output(to allow for validation).
Run script must delete SearchID.
'''
start = datetime.now()
print 'loading context ads'
ctx = get_context_ads()
ctx['LogPrice'] = ctx['Price'].apply(lambda x : round(log(x+1), 1))
ctx['ParamLen'] = ctx['Params'].apply(lambda d : len(d)).fillna(0)
ctx['TitleLen'] = ctx['Title'].apply(lambda s : len(s)).fillna(0)
del ctx['Price']
del ctx['Title']
del ctx['Params']
print 'loading users'
users = sframes.load('user.gl')
print 'loading category and location'
ctg = sframes.load('category.gl')
# Admins said this field could be deleted.
del ctg['SubcategoryID']
loc = sframes.load('location.gl')
print 'small objects loaded, elapsed time: %s' % (datetime.now() - start)
print 'ingesting train.gl'
tr = sframes.load('train.gl')
tr = tr[tr['ObjectType'] == 3]
del tr['ObjectType']
tr['log_ctr'] = tr['HistCTR'].apply(lambda x : -10 * round(log(x), 1))
del tr['HistCTR']
print 'train.gl ingested, elapsed time: %s' % (datetime.now() - start)
# SearchDate: I can't decide what to do with it, so I'm leaving it in,
# as-is. The run script will have to remove it. This allows sorting by
# date without doing this huge join again.
print 'ingesting search.gl'
si = sframes.load('search.gl')
si['SQexists'] = si['SearchQuery'].apply(lambda x : len(x) > 0)
del si['SearchQuery']
si['SPexists'] = (si['SearchParams'].apply(lambda d : int(d is not None))
.fillna(0))
del si['SearchParams']
print 'search.gl ingested, elapsed time: %s' % (datetime.now() - start)
print 'joining user.gl into search.gl'
si = si.join(users, how='left', on='UserID')
print 'user.gl joined in, elapsed time: %s' % (datetime.now() - start)
print 'joining location.gl to search.gl'
si = si.join(loc, how='left', on='LocationID')
print 'location.gl joined in, elapsed time: %s' % (datetime.now() - start)
print 'joining category.gl to search.gl'
si = si.join(ctg, how='left', on='CategoryID')
print 'category.gl joined in, elapsed time: %s' % (datetime.now() - start)
# join category into context ads and rename to avoid name clash
print 'joining category into ads'
ctx = ctx.join(ctg, how='left', on='CategoryID')
ctx.rename({'CategoryID':'AdCat'})
print 'category.gl joined into ads, elapsed time: %s' % (datetime.now() - start)
print 'joining context ads into train'
tr = tr.join(ctx, how='left', on='AdID')
print 'context ads joined into train, elapsed time: %s' % (datetime.now() - start)
print 'joining up training set (search and train)...'
tr = tr.join(si, how='left', on='SearchID')
print 'join completed, elapsed time: %s' % (datetime.now() - start)
print 'sorting train by SearchDate, SearchID, AdID
tr = tr.sort(['SearchDate', 'SearchID', 'AdID'])
path = os.path.join(avito2_io.PROCESSED, 'gl_train2.csv')
print 'saving training features to %s' % path
tr.save(path, format='csv')
print 'training features saved, elapsed time: %s' % (datetime.now() - start)
# test
print 'ingesting test.gl'
test = sframes.load('test.gl')
test = test[test['ObjectType'] == 3]
del test['ObjectType']
test['log_ctr'] = test['HistCTR'].apply(lambda x : -10 * round(log(x), 1))
del test['HistCTR']
print 'test.gl ingested, elapsed time: %s' % (datetime.now() - start)
print 'joining context ads into test'
test = test.join(ctx, how='left', on='AdID')
print 'context ads joined into test, elapsed time: %s' % (datetime.now() - start)
print 'joining up test set...'
ftest = test.join(si, how='left', on='SearchID')
del ftest['SearchID']
print 'join completed, elapsed time: %s' % (datetime.now() - start)
print 'sorting test...'
ftest = ftest.sort('ID')
path = os.path.join(avito2_io.PROCESSED, 'gl_test2.csv')
print 'saving test features to %s' % path
ftest.save(path, format='csv')
print 'finished, elapsed time: %s' % (datetime.now() - start)
def features1():
'''
This function implements and records the construction of the first sframe
features, which are the same as the features used in the pure python/pypy
run1.py. This uses just SearchInfo and trainSearchStream and runs row-wise
on context ads only.
'''
# process trainSearchStream
start = datetime.now()
print 'ingesting train.gl'
tr = sframes.load('train.gl')
tr = tr[tr['ObjectType'] == 3]
del tr['ObjectType']
tr['log_ctr'] = tr['HistCTR'].apply(lambda x : -10 * round(log(x), 1))
del tr['HistCTR']
print 'train.gl ingested, elapsed time: %s' % (datetime.now() - start)
# process SearchInfo
print 'ingesting search.gl'
si = sframes.load('search.gl')
# In run1.py, we didn't use date or IPID
del si['SearchDate']
del si['IPID']
si['SQexists'] = si['SearchQuery'].apply(lambda x : len(x) > 0)
del si['SearchQuery']
# NB: lambda d : 0 if d is None else len(d) doesn't seem to work
si['SPexists'] = (si['SearchParams'].apply(lambda d : int(d is not None))
.fillna(0))
del si['SearchParams']
print 'search.gl ingested, elapsed time: %s' % (datetime.now() - start)
# join up training set
# NB: due to lazy evaluation, this might not time accurately
print 'joining up training set...'
f = tr.join(si, how='left', on='SearchID')
# This line makes validation impossible. Run script must delete SearchID.
#del f['SearchID']
print 'join completed, elapsed time: %s' % (datetime.now() - start)
# save training features
path = os.path.join(avito2_io.PROCESSED, 'gl_train1.csv')
print 'saving training features to %s' % path
f.save(path, format='csv')
print 'training features saved, elapsed time: %s' % (datetime.now() - start)
# load test set
print 'ingesting test.gl'
test = sframes.load('test.gl')
test = test[test['ObjectType'] == 3]
del test['ObjectType']
test['log_ctr'] = test['HistCTR'].apply(lambda x : -10 * round(log(x), 1))
del test['HistCTR']
print 'test.gl ingested, elapsed time: %s' % (datetime.now() - start)
# join up test set
print 'joining up test set...'
ftest = test.join(si, how='left', on='SearchID')
del ftest['SearchID']
print 'join completed, elapsed time: %s' % (datetime.now() - start)
# save test set
path = os.path.join(avito2_io.PROCESSED, 'gl_test1.csv')
print 'saving test features to %s' % path
ftest.save(path, format='csv')
print 'finished, elapsed time: %s' % (datetime.now() - start)
def run_test(model):
test = sframes.load('combo_test.gl')
prepareSFrame(test)
cit = chunk_iterator(test)
pred = predict(cit, model)
return pred
def run_test(model):
test = sframes.load('combo_test.gl')
prepareSFrame(test)
cit = chunk_iterator(test)
pred = predict(cit, model)
return pred
help='# of passes over training data.')
args = parser.parse_args()
if args.users == 'full':
users = build_user_dict()
print('loading full user data')
elif args.users == 'counts':
users = avito2_io.get_artifact('user_counts.pkl')
print('loading user counts only from user_counts.pkl')
elif args.users == 'si':
users = avito2_io.get_artifact('user_si.pkl')
print('loading user dict from user_si.pkl')
else:
users = None
D = 2**args.bits
if args.all:
tr = sframes.load('train_context.gl')
si = sframes.load('search.gl')
if not args.sub:
raise Warning('--all without --sub is not sensible.')
else:
tr = sframes.load('train_ds.gl')
si = sframes.load('search_ds.gl')
# no interactions; it'd take days
model = train(tr, si, args.alpha, args.beta, args.l1, args.l2, D, users,
False, args.maxlines, args.passes)
print('finished training')
if args.all:
offset = 0.0
else:
offset = compute_offset(tr, args.maxlines)
