def import_mp_votes(subset=False):
if MPVote.all().count() > 0:
print "Import already complete"
return
subset_const = [
"Brighton, Kemptown",
"Brighton, Pavillion",
"Hove",
"Hackney South and Shoreditch",
"Edinburgh North, and Leith"
]
subset_mp = [
"Caroline Lucas",
"Simon Kirby",
"Mike Weatherley",
"Meg Hillier",
"Mark Lazarowicz"
]
question_list = {}
csvfile = open('fixtures/mp_votes/vote_questions.csv', 'rU')
for row in csv.reader(csvfile):
d = Question()
d.question = row[0]
d.title = row[1]
d.date = datetime.datetime.now()
d.publicwhip_url = row[3]
d.put()
question_list[row[4]] = d
mps_created = []
consts_created = []
for question in question_list:
print question
csvfile = open('fixtures/mp_votes/%s.csv' % question, 'rU')
for row in csv.reader(csvfile):
if subset and row[1] not in subset_const and row[0] not in subset_mp:
continue
try:
v = MPVote(parent=question_list[question])
v.question = str(question_list[question].key())
v.mp_name = row[0]
v.mp_slug = slugify(row[0])
v.mp_constituency = row[1]
v.mp_party = normalise_party(row[2]).lower()
v.selection = normalise_selection(row[3])
v.mp_whilst = get_whilst(row[2])
v.put()
if v.mp_slug not in mps_created:
mp = MP()
mp.slug = v.mp_slug
mp.name = v.mp_name
mp.constituency = v.mp_constituency
mp.party = v.mp_party
mp.put()
mps_created.append(v.mp_slug)
if v.mp_constituency not in consts_created:
const = Constituency()
const.name = v.mp_constituency
const.slug = slugify(v.mp_constituency)
const.mp_name = v.mp_name
const.mp_party = v.mp_party
const.put()
consts_created.append(v.mp_constituency)
except:
print "Failed insert"
shuffidx) # randomly choose partition of data into test / fold
for fold in range(10):
print('> fold ', fold)
test_bottom = fold * test_size
test_top = (1 + fold) * test_size
test_indices = shuffidx[test_bottom:test_top]
train_indices = shuffidx[0:test_bottom] + shuffidx[test_top:]
loss_func = []
bfs_params = []
test_loss = []
torch.set_rng_state(rng_state) #fix init state
torch.manual_seed(0)
pht = MP(rbf=bfs, filtername='f')
optimizer_filter = optim.SGD([{'params': pht.rbfweights}], lr=lr)
for epoch in range(max_epoch):
pht.train()
np.random.shuffle(train_indices)
lss = 0
for b in range(train_batches):
bfs_params.append(pht.rbfweights.detach().clone().numpy())
pht.train()
train_indices_batch = train_indices[b * batch_size:(b + 1) *
batch_size]
optimizer_filter.zero_grad()
