def map_init(self):
pickle_ = u.pickle_load(self.params['total_file'])
self.totals = pickle_['projects']
# Compute masks (find days with insufficient data)
try:
# use precomputed mask if available (Wikipedia)
self.mask = pickle_['mask']
except KeyError:
# compute a mask for Twitter
pdata = self.totals['t@']
mask = [
tweet.is_enough(pdata['series'].date(i),
pdata['series'][i],
sample_rate=self.params['tw_sample_rate'])
for i in xrange(len(pdata['series']))
]
self.mask = math_.Date_Vector(pdata['series'].first_day,
np.array(mask, dtype=np.bool))
if (self.mask.sum() < 0.5 * len(self.mask)):
u.abort('too many low-data days (%d of %d); check sample rate?' %
(self.mask.sum(), len(self.mask)))
# Read target time series
self.targets = list()
short_names = u.without_common_prefix(self.params['input_sss'])
for (sn, ln) in zip(short_names, self.params['input_sss']):
e = ssheet.Excel(file_=ln)
for (name, (series, mask)) in e.data.iteritems():
name = '%s:%s' % (urllib.quote_plus(u.without_ext(
sn, '.xls')), urllib.quote_plus(name))
self.targets.append({
'name': name,
'series': series,
'mask': mask
})
def map_init(self):
pickle_ = u.pickle_load(self.params['total_file'])
self.totals = pickle_['projects']
# Compute masks (find days with insufficient data)
try:
# use precomputed mask if available (Wikipedia)
self.mask = pickle_['mask']
except KeyError:
# compute a mask for Twitter
pdata = self.totals['t@']
mask = [tweet.is_enough(pdata['series'].date(i),
pdata['series'][i],
sample_rate=self.params['tw_sample_rate'])
for i in xrange(len(pdata['series']))]
self.mask = math_.Date_Vector(pdata['series'].first_day,
np.array(mask, dtype=np.bool))
if (self.mask.sum() < 0.5 * len(self.mask)):
u.abort('too many low-data days (%d of %d); check sample rate?'
% (self.mask.sum(), len(self.mask)))
# Read target time series
self.targets = list()
short_names = u.without_common_prefix(self.params['input_sss'])
for (sn, ln) in zip(short_names, self.params['input_sss']):
e = ssheet.Excel(file_=ln)
for (name, (series, mask)) in e.data.iteritems():
name = '%s:%s' % (urllib.quote_plus(u.without_ext(sn, '.xls')),
urllib.quote_plus(name))
self.targets.append({ 'name': name,
'series': series,
'mask': mask })
def graph_load():
g = u.pickle_load(args.in_ + '/articles/wiki-graph.pkl.gz')
for root in g.keys():
g[root] = {
timeseries.name_url_canonicalize(url): dist
for (url, dist) in g[root].items()
}
return g
def graph_load():
g = u.pickle_load(args.in_ + '/articles/wiki-graph.pkl.gz')
for root in g.keys():
g[root] = { timeseries.name_url_canonicalize(url): dist
for (url, dist) in g[root].items() }
return g
def unshrink_from_disk(self, dir_, model=False, results=False):
assert (model or results)
if (model and isinstance(self.model, u.Deleted_To_Save_Memory)):
self.model = u.pickle_load('%s/model.%d' % (dir_, self.i))
if (results and isinstance(self.results, u.Deleted_To_Save_Memory)):
self.results = u.pickle_load('%s/results.%d' % (dir_, self.i))
def unshrink_from_disk(self, dir_, model=False, results=False):
assert (model or results)
if (model and isinstance(self.model, u.Deleted_To_Save_Memory)):
self.model = u.pickle_load('%s/model.%d' % (dir_, self.i))
if (results and isinstance(self.results, u.Deleted_To_Save_Memory)):
self.results = u.pickle_load('%s/results.%d' % (dir_, self.i))