class Dummy(Symbol):
"""Dummy symbols are each unique, even if they have the same name:
>>> from sympy import Dummy
>>> Dummy("x") == Dummy("x")
False
If a name is not supplied then a string value of an internal count will be
used. This is useful when a temporary variable is needed and the name
of the variable used in the expression is not important.
>>> Dummy() #doctest: +SKIP
_Dummy_10
"""
# In the rare event that a Dummy object needs to be recreated, both the
# `name` and `dummy_index` should be passed. This is used by `srepr` for
# example:
# >>> d1 = Dummy()
# >>> d2 = eval(srepr(d1))
# >>> d2 == d1
# True
#
# If a new session is started between `srepr` and `eval`, there is a very
# small chance that `d2` will be equal to a previously-created Dummy.
_count = 0
_prng = random.Random()
_base_dummy_index = _prng.randint(10**6, 9*10**6)
__slots__ = ('dummy_index',)
is_Dummy = True
def __new__(cls, name=None, dummy_index=None, **assumptions):
if dummy_index is not None:
assert name is not None, "If you specify a dummy_index, you must also provide a name"
if name is None:
name = "Dummy_" + str(Dummy._count)
if dummy_index is None:
dummy_index = Dummy._base_dummy_index + Dummy._count
Dummy._count += 1
cls._sanitize(assumptions, cls)
obj = Symbol.__xnew__(cls, name, **assumptions)
obj.dummy_index = dummy_index
return obj
def __getstate__(self):
return {'_assumptions': self._assumptions, 'dummy_index': self.dummy_index}
@cacheit
def sort_key(self, order=None):
return self.class_key(), (
2, (self.name, self.dummy_index)), S.One.sort_key(), S.One
def _hashable_content(self):
return Symbol._hashable_content(self) + (self.dummy_index,)
def main(args):
def worker_init_fn(worker_id):
np.random.seed(args.random_seed + worker_id)
n_gpu = 0
if torch.cuda.is_available():
n_gpu = torch.cuda.device_count()
np.random.seed(args.random_seed)
random.seed(args.random_seed)
rng = random.Random(args.random_seed)
torch.manual_seed(args.random_seed)
if n_gpu > 0:
torch.cuda.manual_seed(args.random_seed)
torch.cuda.manual_seed_all(args.random_seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
ontology = json.load(open(args.ontology_data))
slot_meta, ontology = make_slot_meta(ontology)
op2id = OP_SET[args.op_code]
# print(op2id)
tokenizer = BertTokenizer(args.vocab_path, do_lower_case=True)
train_data_raw = prepare_dataset(data_path=args.train_data_path,
tokenizer=tokenizer,
slot_meta=slot_meta,
n_history=args.n_history,
max_seq_length=args.max_seq_length,
op_code=args.op_code,
turn_weight=args.turn_weight,
seq_num=args.seq_num)
train_data = MultiWozDataset(train_data_raw,
tokenizer,
slot_meta,
args.max_seq_length,
rng,
ontology,
args.word_dropout,
args.shuffle_state,
args.shuffle_p)
print("# train examples %d" % len(train_data_raw))
dev_data_raw = prepare_dataset(data_path=args.dev_data_path,
tokenizer=tokenizer,
slot_meta=slot_meta,
n_history=args.n_history,
max_seq_length=args.max_seq_length,
op_code=args.op_code)
print("# dev examples %d" % len(dev_data_raw))
test_data_raw = prepare_dataset(data_path=args.test_data_path,
tokenizer=tokenizer,
slot_meta=slot_meta,
n_history=args.n_history,
max_seq_length=args.max_seq_length,
op_code=args.op_code)
print("# test examples %d" % len(test_data_raw))
model_config = BertConfig.from_json_file(args.bert_config_path)
model_config.dropout = args.dropout
model_config.attention_probs_dropout_prob = args.attention_probs_dropout_prob
model_config.hidden_dropout_prob = args.hidden_dropout_prob
model = SomDST(model_config, len(op2id), len(domain2id), op2id['update'], args.exclude_domain)
if not os.path.exists(args.bert_ckpt_path):
args.bert_ckpt_path = download_ckpt(args.bert_ckpt_path, args.bert_config_path, 'assets')
ckpt = torch.load(args.bert_ckpt_path, map_location='cpu')
model.encoder.bert.load_state_dict(ckpt)
# re-initialize added special tokens ([SLOT], [NULL], [EOS])
model.encoder.bert.embeddings.word_embeddings.weight.data[1].normal_(mean=0.0, std=0.02)
model.encoder.bert.embeddings.word_embeddings.weight.data[2].normal_(mean=0.0, std=0.02)
model.encoder.bert.embeddings.word_embeddings.weight.data[3].normal_(mean=0.0, std=0.02)
model.to(device)
num_train_steps = int(len(train_data_raw) / args.batch_size * args.n_epochs)
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
enc_param_optimizer = list(model.encoder.named_parameters())
enc_optimizer_grouped_parameters = [
{'params': [p for n, p in enc_param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
{'params': [p for n, p in enc_param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
enc_optimizer = AdamW(enc_optimizer_grouped_parameters, lr=args.enc_lr)
enc_scheduler = WarmupLinearSchedule(enc_optimizer, int(num_train_steps * args.enc_warmup),
t_total=num_train_steps)
dec_param_optimizer = list(model.decoder.parameters())
dec_optimizer = AdamW(dec_param_optimizer, lr=args.dec_lr)
dec_scheduler = WarmupLinearSchedule(dec_optimizer, int(num_train_steps * args.dec_warmup),
t_total=num_train_steps)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=args.batch_size,
collate_fn=train_data.collate_fn,
num_workers=args.num_workers,
worker_init_fn=worker_init_fn)
loss_fnc = nn.CrossEntropyLoss(reduction="none")
best_score = {'epoch': 0, 'joint_acc': float("-inf"), 'op_acc': 0, 'final_slot_f1': 0}
for epoch in range(args.n_epochs):
batch_loss = []
model.train()
for step, batch in enumerate(train_dataloader):
batch = [b.to(device) if not isinstance(b, int) else b for b in batch]
input_ids, input_mask, segment_ids, state_position_ids, op_ids, \
domain_ids, gen_ids, max_value, max_update, turn_weights = batch
if rng.random() < args.decoder_teacher_forcing: # teacher forcing
teacher = gen_ids
else:
teacher = None
domain_scores, state_scores, gen_scores = model(input_ids=input_ids,
token_type_ids=segment_ids,
state_positions=state_position_ids,
attention_mask=input_mask,
max_value=max_value,
op_ids=op_ids,
max_update=max_update,
teacher=teacher)
# print(op_ids.view(-1))
# print(gen_scores)
loss_s = loss_fnc(state_scores.view(-1, len(op2id)), op_ids.view(-1))
loss_g = masked_cross_entropy_for_value(gen_scores.contiguous(),
gen_ids.contiguous(), turn_weights,
tokenizer.vocab['[PAD]'])
# there are 30 slots
loss_s = loss_s.reshape(turn_weights.size()[0], 30) * turn_weights.repeat_interleave(30).reshape(
turn_weights.size()[0], 30)
loss_s = loss_s.mean()
loss = loss_s + loss_g
if args.exclude_domain is not True:
loss_d = loss_fnc(domain_scores.view(-1, len(domain2id)), domain_ids.view(-1)).mean()
# print(turn_weights)
# print(loss_s.size(), loss_d.size(), loss_g.size())
loss = loss + loss_d
batch_loss.append(loss.item())
loss.backward()
enc_optimizer.step()
enc_scheduler.step()
dec_optimizer.step()
dec_scheduler.step()
model.zero_grad()
if step % 100 == 0:
if args.exclude_domain is not True:
print("[%d/%d] [%d/%d] mean_loss : %.3f, state_loss : %.3f, gen_loss : %.3f, dom_loss : %.3f" \
% (epoch + 1, args.n_epochs, step,
len(train_dataloader), np.mean(batch_loss),
loss_s.item(), loss_g.item(), loss_d.item()))
else:
print("[%d/%d] [%d/%d] mean_loss : %.3f, state_loss : %.3f, gen_loss : %.3f" \
% (epoch + 1, args.n_epochs, step,
len(train_dataloader), np.mean(batch_loss),
loss_s.item(), loss_g.item()))
batch_loss = []
if (epoch + 1) % args.eval_epoch == 0:
eval_res = model_evaluation(model, dev_data_raw, tokenizer, slot_meta, epoch + 1, args.op_code,mode="dev")
if eval_res['joint_acc'] > best_score['joint_acc']:
best_score = eval_res
model_to_save = model.module if hasattr(model, 'module') else model
save_path = os.path.join(args.save_dir, 'model_best.bin')
torch.save(model_to_save.state_dict(), save_path)
print("Best Score : ", best_score)
print("\n")
print("Test using best model...")
best_epoch = best_score['epoch']
ckpt_path = os.path.join(args.save_dir, 'model_best.bin')
model = SomDST(model_config, len(op2id), len(domain2id), op2id['update'], args.exclude_domain)
ckpt = torch.load(ckpt_path, map_location='cpu')
model.load_state_dict(ckpt)
model.to(device)
model_evaluation(model, test_data_raw, tokenizer, slot_meta, best_epoch, args.op_code,
is_gt_op=False, is_gt_p_state=False, is_gt_gen=False, save_dir=args.save_dir, mode="test")
for k, v in inputs_dict.items():
if torch.is_tensor(v):
inputs_dict[k] = v.to(0)
for model_class in self.all_model_classes:
model = model_class(config=config)
model.to(0)
model.eval()
# Wrap model in nn.DataParallel
model = torch.nn.DataParallel(model)
with torch.no_grad():
_ = model(**self._prepare_for_class(inputs_dict, model_class))
global_rng = random.Random()
def ids_tensor(shape, vocab_size, rng=None, name=None):
# Creates a random int32 tensor of the shape within the vocab size
if rng is None:
rng = global_rng
total_dims = 1
for dim in shape:
total_dims *= dim
values = []
for _ in range(total_dims):
values.append(rng.randint(0, vocab_size - 1))
def main(args):
# ----- solver and variable declaration -----
(nb_slabs, capacity, max_capacity, weights, colors, loss, color_orders) =\
ReadData(args.data)
nb_colors = len(color_orders)
solver = pywrapcp.Solver('Steel Mill Slab')
x = [solver.IntVar(0, nb_slabs - 1, 'x' + str(i)) for i in range(nb_slabs)]
load_vars = [
solver.IntVar(0, max_capacity - 1, 'load_vars' + str(i))
for i in range(nb_slabs)
]
# ----- post of the constraints -----
# Bin Packing.
BinPacking(solver, x, weights, load_vars)
# At most two colors per slab.
for s in range(nb_slabs):
solver.Add(
solver.SumLessOrEqual([
solver.Max([solver.IsEqualCstVar(x[c], s) for c in o])
for o in color_orders
], 2))
# ----- Objective -----
objective_var = \
solver.Sum([load_vars[s].IndexOf(loss) for s in range(nb_slabs)]).Var()
objective = solver.Minimize(objective_var, 1)
# ----- start the search and optimization -----
assign_db = SteelDecisionBuilder(x, nb_slabs, weights, loss, load_vars)
first_solution = solver.Assignment()
first_solution.Add(x)
first_solution.AddObjective(objective_var)
store_db = solver.StoreAssignment(first_solution)
first_solution_db = solver.Compose([assign_db, store_db])
print('searching for initial solution,', end=' ')
solver.Solve(first_solution_db)
print('initial cost =', first_solution.ObjectiveValue())
# To search a fragment, we use a basic randomized decision builder.
# We can also use assign_db instead of inner_db.
inner_db = solver.Phase(x, solver.CHOOSE_RANDOM, solver.ASSIGN_MIN_VALUE)
# The most important aspect is to limit the time exploring each fragment.
inner_limit = solver.FailuresLimit(args.lns_fail_limit)
continuation_db = solver.SolveOnce(inner_db, [inner_limit])
# Now, we create the LNS objects.
rand = random.Random()
rand.seed(args.lns_random_seed)
local_search_operator = SteelRandomLns(x, rand, args.lns_fragment_size)
# This is in fact equivalent to the following predefined LNS operator:
# local_search_operator = solver.RandomLNSOperator(x,
# args.lns_fragment_size,
# args.lns_random_seed)
local_search_parameters = solver.LocalSearchPhaseParameters(
local_search_operator, continuation_db)
local_search_db = solver.LocalSearchPhase(first_solution,
local_search_parameters)
global_limit = solver.TimeLimit(args.time_limit)
print('using LNS to improve the initial solution')
search_log = solver.SearchLog(100000, objective_var)
solver.NewSearch(local_search_db, [objective, search_log, global_limit])
while solver.NextSolution():
print('Objective:', objective_var.Value(),\
'check:', sum(loss[load_vars[s].Min()] for s in range(nb_slabs)))
solver.EndSearch()
from snake_game import SnakeGame
import pygame
import time
import random
rand = random.Random()
class SnakeGameGUI(SnakeGame):
def __init__(self, headless_mode=False):
super().__init__()
self.BLUE = (0, 0, 255)
self.PURPLE = (255, 0, 255)
self.BLACK = (0, 0, 0)
self.RED = (255, 0, 0)
self.SQUARESIZE = 10
self.WIDTH = self.SQUARESIZE * self.width
self.HEIGHT = self.SQUARESIZE * self.height
self.SIZE = (self.WIDTH + 400, self.HEIGHT)
if headless_mode == False:
self.SCREEN = pygame.display.set_mode(self.SIZE)
pygame.init()
def draw_board(self):
myfont = pygame.font.SysFont("monospace", 50)
self.SCREEN.fill(self.BLACK)
for i in range(self.height):
for j in range(self.width):
# check for head, body, food
if self.board[i, j] == 1:
def test_contract_receive_channelnew_must_be_idempotent():
block_number = 10
pseudo_random_generator = random.Random()
token_network_id = factories.make_address()
token_id = factories.make_address()
token_network_state = TokenNetworkState(token_network_id, token_id)
payment_network_identifier = factories.make_payment_network_identifier()
amount = 30
our_balance = amount + 50
channel_state1 = factories.make_channel(our_balance=our_balance)
channel_state2 = copy.deepcopy(channel_state1)
state_change1 = ContractReceiveChannelNew(
factories.make_transaction_hash(),
token_network_id,
channel_state1,
)
token_network.state_transition(
payment_network_identifier,
token_network_state,
state_change1,
pseudo_random_generator,
block_number,
)
# change the existing channel
payment_identifier = 1
message_identifier = random.randint(0, UINT64_MAX)
channel.send_directtransfer(
channel_state1,
amount,
message_identifier,
payment_identifier,
)
state_change2 = ContractReceiveChannelNew(
factories.make_transaction_hash(),
token_network_id,
channel_state2,
)
# replay the ContractReceiveChannelNew state change
iteration = token_network.state_transition(
payment_network_identifier,
token_network_state,
state_change2,
pseudo_random_generator,
block_number,
)
msg = 'the channel must not been overwritten'
channelmap_by_id = iteration.new_state.channelidentifiers_to_channels
assert channelmap_by_id[channel_state1.identifier] == channel_state1, msg
channelmap_by_address = iteration.new_state.partneraddresses_to_channels
partner_channels = channelmap_by_address[
channel_state1.partner_state.address]
assert partner_channels[channel_state1.identifier] == channel_state1, msg
def test_routing_updates(
token_network_state,
our_address,
):
open_block_number = 10
pseudo_random_generator = random.Random()
pkey1, address1 = factories.make_privkey_address()
pkey2, address2 = factories.make_privkey_address()
pkey3, address3 = factories.make_privkey_address()
amount = 30
our_balance = amount + 50
channel_state = factories.make_channel(
our_balance=our_balance,
our_address=our_address,
partner_balance=our_balance,
partner_address=address1,
)
payment_network_identifier = factories.make_payment_network_identifier()
# create a new channel as participant, check graph update
channel_new_state_change = ContractReceiveChannelNew(
transaction_hash=factories.make_transaction_hash(),
token_network_identifier=token_network_state.address,
channel_state=channel_state,
)
channel_new_iteration1 = token_network.state_transition(
payment_network_identifier=payment_network_identifier,
token_network_state=token_network_state,
state_change=channel_new_state_change,
pseudo_random_generator=pseudo_random_generator,
block_number=open_block_number,
)
graph_state = channel_new_iteration1.new_state.network_graph
assert channel_state.identifier in graph_state.channel_identifier_to_participants
assert len(graph_state.channel_identifier_to_participants) == 1
assert graph_state.network[our_address][address1] is not None
assert len(graph_state.network.edges()) == 1
# create a new channel without being participant, check graph update
new_channel_identifier = factories.make_channel_identifier()
channel_new_state_change = ContractReceiveRouteNew(
transaction_hash=factories.make_transaction_hash(),
token_network_identifier=token_network_state.address,
channel_identifier=new_channel_identifier,
participant1=address2,
participant2=address3,
)
channel_new_iteration2 = token_network.state_transition(
payment_network_identifier=payment_network_identifier,
token_network_state=channel_new_iteration1.new_state,
state_change=channel_new_state_change,
pseudo_random_generator=pseudo_random_generator,
block_number=open_block_number + 10,
)
graph_state = channel_new_iteration2.new_state.network_graph
assert channel_state.identifier in graph_state.channel_identifier_to_participants
assert new_channel_identifier in graph_state.channel_identifier_to_participants
assert len(graph_state.channel_identifier_to_participants) == 2
assert graph_state.network[our_address][address1] is not None
assert graph_state.network[address2][address3] is not None
assert len(graph_state.network.edges()) == 2
# close the channel the node is a participant of, check edge is removed from graph
closed_block_number = open_block_number + 20
channel_close_state_change1 = ContractReceiveChannelClosed(
transaction_hash=factories.make_transaction_hash(),
transaction_from=channel_state.partner_state.address,
token_network_identifier=token_network_state.address,
channel_identifier=channel_state.identifier,
closed_block_number=closed_block_number,
)
channel_closed_iteration1 = token_network.state_transition(
payment_network_identifier=payment_network_identifier,
token_network_state=channel_new_iteration2.new_state,
state_change=channel_close_state_change1,
pseudo_random_generator=pseudo_random_generator,
block_number=closed_block_number,
)
# Check that a second ContractReceiveChannelClosed events is handled properly
# This might have been sent from the other participant of the channel
# See issue #2449
channel_close_state_change2 = ContractReceiveChannelClosed(
transaction_hash=factories.make_transaction_hash(),
transaction_from=channel_state.our_state.address,
token_network_identifier=token_network_state.address,
channel_identifier=channel_state.identifier,
closed_block_number=closed_block_number,
)
channel_closed_iteration2 = token_network.state_transition(
payment_network_identifier=payment_network_identifier,
token_network_state=channel_closed_iteration1.new_state,
state_change=channel_close_state_change2,
pseudo_random_generator=pseudo_random_generator,
block_number=closed_block_number,
)
graph_state = channel_closed_iteration2.new_state.network_graph
assert channel_state.identifier not in graph_state.channel_identifier_to_participants
assert new_channel_identifier in graph_state.channel_identifier_to_participants
assert len(graph_state.channel_identifier_to_participants) == 1
assert graph_state.network[address2][address3] is not None
assert len(graph_state.network.edges()) == 1
# close the channel the node is not a participant of, check edge is removed from graph
channel_close_state_change3 = ContractReceiveRouteClosed(
transaction_hash=factories.make_transaction_hash(),
token_network_identifier=token_network_state.address,
channel_identifier=new_channel_identifier,
)
channel_closed_iteration3 = token_network.state_transition(
payment_network_identifier=payment_network_identifier,
token_network_state=channel_closed_iteration2.new_state,
state_change=channel_close_state_change3,
pseudo_random_generator=pseudo_random_generator,
block_number=closed_block_number + 10,
)
# Check that a second ContractReceiveRouteClosed events is handled properly.
# This might have been sent from the second participant of the channel
# See issue #2449
channel_close_state_change4 = ContractReceiveRouteClosed(
transaction_hash=factories.make_transaction_hash(),
token_network_identifier=token_network_state.address,
channel_identifier=new_channel_identifier,
)
channel_closed_iteration4 = token_network.state_transition(
payment_network_identifier=payment_network_identifier,
token_network_state=channel_closed_iteration3.new_state,
state_change=channel_close_state_change4,
pseudo_random_generator=pseudo_random_generator,
block_number=closed_block_number + 10,
)
graph_state = channel_closed_iteration4.new_state.network_graph
assert channel_state.identifier not in graph_state.channel_identifier_to_participants
assert new_channel_identifier not in graph_state.channel_identifier_to_participants
assert len(graph_state.channel_identifier_to_participants) == 0
assert len(graph_state.network.edges()) == 0
def addition(G, num_walks, walklength, walks, es, edge, ind):
G.add_edge(*edge, weight=1)
node_i = str(edge[0])
node_j = str(edge[1])
allwalks_i = helpers.scan(
client=es,
query={"query": {
"match_phrase": {
"wlks": {
"query": node_i
}
}
}},
index=ind,
size=10000,
scroll='1m')
allwalks_i = list(allwalks_i)
degree_i = G.degree(edge[0])
degree_j = G.degree(edge[1])
sampled_i = random.sample(allwalks_i, int(len(allwalks_i) / degree_i))
blk = []
if len(sampled_i) > 0:
for wk in sampled_i:
wk_id = int(wk['_id'])
wk_pos = random.choice(
es.termvectors(index=ind,
doc_type='walk',
id=wk_id,
field_statistics=False,
term_statistics=False,
offsets=False,
positions=True,
fields=['wlks'])['term_vectors']['wlks']
['terms'][node_i]['tokens'])['position']
wk_pos = wk_pos % 100
walks[wk_id][wk_pos + 1:] = random_walk(G,
walklength - wk_pos - 1,
alpha=0,
rand=random.Random(),
start=edge[1])
action = {
"_op_type": 'update',
"_index": ind,
"_type": "walk",
"_id": int(wk['_id']),
"_source": {
"doc": {
"wlks": ' '.join((walks[wk_id]))
}
}
}
blk.append(action)
helpers.bulk(es, blk)
es.indices.refresh(index=ind)
allwalks_j = helpers.scan(
client=es,
query={"query": {
"match_phrase": {
"wlks": {
"query": node_j
}
}
}},
index=ind,
size=10000,
scroll='1m')
allwalks_j = list(allwalks_j)
sampled_j = random.sample(allwalks_j, int(len(allwalks_j) / degree_j))
blk = []
if len(sampled_j) > 0:
for wk in sampled_j:
wk_id = int(wk['_id'])
wk_pos = random.choice(
es.termvectors(index=ind,
doc_type='walk',
id=wk_id,
field_statistics=False,
term_statistics=False,
offsets=False,
positions=True,
fields=['wlks'])['term_vectors']['wlks']
['terms'][node_j]['tokens'])['position']
wk_pos = wk_pos % 100
walks[wk_id][wk_pos + 1:] = random_walk(G,
walklength - wk_pos - 1,
alpha=0,
rand=random.Random(),
start=edge[0])
action = {
"_op_type": 'update',
"_index": ind,
"_type": "walk",
"_id": int(wk['_id']),
"_source": {
"doc": {
"wlks": ' '.join((walks[wk_id]))
}
}
}
blk.append(action)
helpers.bulk(es, blk)
blk = []
if len(allwalks_i) == 0:
print 'node addition'
for kk in range(num_walks):
random.seed(kk)
walks.append(
random_walk(G,
walklength,
alpha=0,
rand=random.Random(),
start=edge[0]))
action = {
"_op_type": 'create',
"_index": ind,
"_type": "walk",
"_id": len(walks),
"_source": {
"doc": {
"wlks": ' '.join((walks[-1]))
}
}
}
blk.append(action)
if len(allwalks_j) == 0:
print 'node addition'
for kk in range(num_walks):
random.seed(kk)
walks.append(
random_walk(G,
walklength,
alpha=0,
rand=random.Random(),
start=edge[1]))
action = {
"_op_type": 'create',
"_index": ind,
"_type": "walk",
"_id": len(walks),
"_source": {
"doc": {
"wlks": ' '.join((walks[-1]))
}
}
}
blk.append(action)
helpers.bulk(es, blk)
es.indices.refresh(index=ind)
print 'updated!'
return walks, G
def pre_mainloop(self):
## logging
assert (len(self.log_filename) != 0
) # 'log_filename' must not be empty string!
logger.setLevel(logging.ERROR)
handler = logging.FileHandler(self.log_filename, 'w')
handler.setLevel(logging.INFO)
formatter = logging.Formatter("%(asctime)s: %(message)s")
handler.setFormatter(formatter)
self.logger.setLevel(logging.INFO)
self.logger.addHandler(handler)
self._nr_elements = 6
self._idx_backdoor = 5
self._init_classifier_output()
self._classified_element = -1
self._classified_letter = -1
for s in self.desired_phrase:
assert s in [l for ls in self.letter_set
for l in ls] # invalid letters in desired phrase!
self._spelled_phrase = ""
self._spelled_letters = ""
self._desired_letters = self.desired_phrase
self._copyspelling_finished = False
# if self.offline:
# self.__idle() # In offline mode: set the first to-be-spelled letter
self._spellerHeight = self.geometry[3] - self.letterbox_size[1]
self._centerPos = (self.geometry[2] / 2., self._spellerHeight / 2.)
self._nr_letters = 0
for i in xrange(len(self.letter_set)):
self._nr_letters += len(self.letter_set[i])
self._current_level = 1 # Index of current level
self._current_sequence = 0 # Index of current sequence
self._current_stimulus = 0 # Index of current stimlus
self._current_countdown = self.nCountdown
self.random = random.Random(clock())
self._debug_classified = None
## init states:
self._state_countdown = True
if not self.countdown_level1:
self._state_countdown = False
self._state_trial = True
else:
#self._state_countdown = not self.offline
self._state_trial = False
self._state_classify = False
self._state_feedback = False
self._state_abort = False
## init containers for VE elements:
self._ve_elements = []
## oscillator state:
if not self.use_oscillator:
self.osc_color = self.bg_color
self.osc_size = 0
## call subclass-specific pre_mainloop:
self.prepare_mainloop()
## build screen elements:
self.__init_screen()
if self.offline:
self.__idle()
if self.abort_trial:
'''
Start listener for abort_trial event eg.
'''
## delay after play (might be useful for filters...)
pygame.time.wait(int(self.wait_after_start * 1000))
self.logger.info("waiting %d seconds after play." %
self.wait_after_start)
## send start trigger:
self.send_parallel(marker.RUN_START)
self.logger.info("[TRIGGER] %d" % marker.RUN_START)
## error potential classifier:
self._ErrP_classifier = None
spread_function_name = "monte_carlo"
models = ["IC", "WC"]
# output directories
degree_dist_dir = "../experiments/datasets/degree_distributions/"
datasets_dir = "../experiments/datasets/"
ground_truth_dir = "../experiments/ground_truth/"
# compute the ground truth for each seed set size
for k in K:
for dataset_name in dataset_names:
max_trials = 100
G = load_graph(g_type=dataset_name)
prng = random.Random(seed)
if community:
G_sampled1 = sampler.random_walk_sampling_with_fly_back(
G, nodes / 2, 0.15, prng)
G_sampled2 = sampler.random_walk_sampling_with_fly_back(
G, nodes / 2, 0.15, prng)
# compose two graphs together
G_sampled = nx.compose(G_sampled1, G_sampled2)
# while nodes in common keep sampling
while len(G_sampled) < nodes and max_trials > 0:
G_sampled1 = sampler.random_walk_sampling_with_fly_back(
G, nodes / 2, 0.15, prng)
G_sampled2 = sampler.random_walk_sampling_with_fly_back(
G, nodes / 2, 0.15, prng)
# compose two graphs together
G_sampled = nx.compose(G_sampled1, G_sampled2)
def deletion(G, num_walks, walklength, walks, es, edge, ind):
G.remove_edge(*edge)
node_i = str(edge[0])
node_j = str(edge[1])
es.indices.refresh(index=ind)
allwalks_i = helpers.scan(client=es,
query={
"query": {
"match_phrase": {
"wlks": {
"query": node_i + " " + node_j
}
}
}
},
index=ind,
size=10000,
scroll='1m')
degree_i = G.degree(edge[0])
degree_j = G.degree(edge[1])
blk = []
for wk in allwalks_i:
wk_id = int(wk['_id'])
wk_pos = (es.termvectors(index=ind,
doc_type='walk',
id=wk_id,
field_statistics=False,
term_statistics=False,
offsets=False,
positions=True,
fields=['wlks'])['term_vectors']['wlks']
['terms'][node_i]['tokens'][0]['position'])
change = int(wk_pos) % 100
#node deletion change
if degree_i == 0 & change == 0:
action = {
"_op_type": 'delete',
"_index": ind,
"_type": "walk",
"_id": wk_id
}
blk.append(action)
helpers.bulk(es, blk)
es.indices.refresh(index=ind)
blk = []
elif degree_i == 0 & change != 0:
walks[wk_id][(change):] = random_walk(G,
walklength - change,
alpha=0,
rand=random.Random(),
start=i[1])
action = {
"_op_type": 'update',
"_index": ind,
"_type": "walk",
"_id": wk_id,
"_source": {
"doc": {
"wlks": " ".join(walks[wk_id])
}
}
}
blk.append(action)
helpers.bulk(es, blk)
es.indices.refresh(index=ind)
blk = []
else:
walks[wk_id][(change):] = random_walk(G,
walklength - change,
alpha=0,
rand=random.Random(),
start=i[0])
action = {
"_op_type": 'update',
"_index": ind,
"_type": "walk",
"_id": wk_id,
"_source": {
"doc": {
"wlks": " ".join(walks[wk_id])
}
}
}
blk.append(action)
helpers.bulk(es, blk)
es.indices.refresh(index=ind)
blk = []
es.indices.refresh(index=ind)
allwalks_j = helpers.scan(client=es,
query={
"query": {
"match_phrase": {
"wlks": {
"query": node_j + " " + node_i
}
}
}
},
index=ind,
size=10000,
scroll='1m')
for wk in allwalks_j:
wk_id = int(wk['_id'])
wk_pos = (es.termvectors(index=ind,
doc_type='walk',
id=wk_id,
field_statistics=False,
term_statistics=False,
offsets=False,
positions=True,
fields=['wlks'])['term_vectors']['wlks']
['terms'][node_j]['tokens'][0]['position'])
change = int(wk_pos) % 100
if degree_j == 0 & change == 0:
action = {
"_op_type": 'delete',
"_index": ind,
"_type": "walk",
"_id": wk_id
}
blk.append(action)
helpers.bulk(es, blk)
es.indices.refresh(index=ind)
blk = []
elif degree_j == 0 & change != 0:
walks[wk_id][int(change):] = random_walk(G,
walklength - change,
alpha=0,
rand=random.Random(),
start=i[0])
action = {
"_op_type": 'update',
"_index": ind,
"_type": "walk",
"_id": wk_id,
"_source": {
"doc": {
"wlks": " ".join(walks[wk_id])
}
}
}
blk.append(action)
helpers.bulk(es, blk)
es.indices.refresh(index=ind)
blk = []
else:
walks[wk_id][int(change):] = random_walk(G,
walklength - change,
alpha=0,
rand=random.Random(),
start=i[1])
action = {
"_op_type": 'update',
"_index": ind,
"_type": "walk",
"_id": wk_id,
"_source": {
"doc": {
"wlks": " ".join(walks[wk_id])
}
}
}
blk.append(action)
helpers.bulk(es, blk)
es.indices.refresh(index=ind)
blk = []
return walks, G
gamma = 0.0002
lembda = 0.0003
utility_matrix = np.zeros((no_of_users,no_of_movies))
user_lfm = np.random.random((no_of_users,no_of_latent_factors))
movie_lfm = np.random.random((no_of_movies,no_of_latent_factors))
df = pd.read_csv('ratings10k.csv', delim_whitespace=False, sep=',', header=None)
df = df.drop(df.index[0])
df = df.values.tolist()
# shuffle
random.Random(4).shuffle(df)
# random.Random(4).shuffle(df2)
# divide in test and train
train, test = train_test_split(df, test_size=0.2)
def createUtilityMatrix():
for each in train: # creating rating matrix
userId = int(int(each[0]) - 1)
movieId = int(int(each[1]) - 1)
def get_random_string(seed):
"""Generates a random string based on the given seed"""
choices = string.ascii_letters + string.digits + string.punctuation
seed = seed.encode("utf-8")
rand = random.Random(seed)
return [rand.choice(choices) for i in range(16)]
def train(self, train_file, lexicon_file=None, freq_file=None, test_prop=0.1, output_importances=False, dump_model=False,
cross_val_test=False, output_errors=False, ablations=None, dump_transformed_data=False, do_shuffle=True, conf=None):
"""
:param train_file: File with segmentations to train on in one of the two formats described in make_prev_next()
:param lexicon_file: Tab delimited lexicon file with full forms in first column and POS tag in second column (multiple rows per form possible)
:param freq_file: Tab delimited file with segment forms and their frequencies as integers in two columns
:param conf: configuration file for training (by default: <MODELNAME>.conf)
:param test_prop: (0.0 -- 0.99) Proportion of shuffled data to test on
:param output_importances: Whether to print feature importances (only if test proportion > 0.0)
:param dump_model: Whether to dump trained model to disk via joblib
:param cross_val_test: Whether to perform cross-validation for hyper parameter optimization
:param output_errors: Whether to output prediction errors to a file 'errs.txt'
:param ablations: Comma separated string of feature names to ablate, e.g. "freq_ratio,prev_grp_pos,next_grp_pos"
:param dump_transformed_data: If true, transform data to a pandas dataframe and write to disk, then quit
(useful to train other approaches on the same features, e.g. a DNN classifier)
:param do_shuffle: Whether training data is shuffled after context extraction but before test partition is created
(this has no effect if training on whole training corpus)
:return: None
"""
import timing
pos_lookup = read_lex(self.short_pos,lexicon_file)
self.read_conf_file(file_name=conf)
self.pos_lookup = pos_lookup
conf_file_parser = self.conf_file_parser
letter_config = LetterConfig(self.letters, self.conf["vowels"], self.pos_lookup)
np.random.seed(42)
if lexicon_file is None:
print("i WARN: No lexicon file provided, learning purely from examples")
seg_table = io.open(train_file,encoding="utf8").read()
seg_table = seg_table.replace("\r","").strip()
for c in self.conf["diacritics"]: # TODO: configurable diacritic removal
pass
#seg_table = seg_table.replace(c,"")
seg_table = seg_table.split("\n")
sys.stderr.write("o Encoding Training data\n")
# Validate training data
non_tab_lines = 0
non_tab_row = 0
for r, line in enumerate(seg_table):
if line.count("\t") < 1:
non_tab_lines += 1
non_tab_row = r
if non_tab_lines > 0:
sys.stderr.write("FATAL: found " + str(non_tab_lines) + " rows in training data not containing tab\n")
sys.stderr.write(" Last occurrence at line: " + str(non_tab_row) + "\n")
sys.exit()
# Make into four cols: prev \t next \t current \t segmented (unless already receiving such a table, for shuffled datasets)
if seg_table[0].count("\t") == 1:
seg_table = make_prev_next(seg_table)
# Ensure OOV symbol is in data
seg_table = ["_\t_\t_\t_"] + seg_table
data_y = []
words = []
all_encoded_groups = []
encoding_cache = {}
non_ident_segs = 0
shuffle_mapping = list(range(len(seg_table)))
zipped = list(zip(seg_table, shuffle_mapping))
# Shuffle table to sample across entire dataset if desired
if do_shuffle and False:
random.Random(24).shuffle(zipped)
seg_table, shuffle_mapping = zip(*zipped)
headers = bg2array("_________",prev_group="_",next_group="_",print_headers=True,is_test=1,grp_id=1,config=letter_config)
word_idx = -1
bug_rows = []
freqs = defaultdict(float)
total_segs = 0.0
flines = io.open(freq_file,encoding="utf8").read().replace("\r","").split("\n") if freq_file is not None else []
for l in flines:
if l.count("\t")==1:
w, f = l.split("\t")
freqs[w] += float(f)
total_segs += float(f)
for u in freqs:
freqs[u] = freqs[u]/total_segs
# Don't use freqs if they're empty
if len(freqs) == 0:
sys.stderr.write("o No segment frequencies provided, adding 'freq_ratio' to ablated features\n")
if ablations is None:
ablations = "freq_ratio"
else:
if "freq_ratio" not in ablations:
ablations += ",freq_ratio"
step = int(1/test_prop) if test_prop > 0 else 0
test_indices = list(range(len(seg_table)))[0::step] if step > 0 else []
test_rows = []
for row_idx, row in enumerate(seg_table):
is_test = 1 if row_idx in test_indices else 0
prev_group, next_group, bound_group, segmentation = row.split("\t")
if bound_group != "|":
if len(bound_group) != len(segmentation.replace("|","")): # Ignore segmentations that also normalize
non_ident_segs += 1
bug_rows.append(row_idx)
continue
###
if dump_transformed_data:
if is_test:
test_rows.append(bound_group + "\t" + segmentation)
###
word_idx += 1
words.append(bound_group)
group_type = "_".join([x for x in [prev_group, next_group, bound_group] if x != ""])
if group_type in encoding_cache: # No need to encode, an identical featured group has already been seen
encoded_group = encoding_cache[group_type]
for c in encoded_group:
c[headers.index("is_test")] = is_test # Make sure that this group's test index is correctly assigned
else:
encoded_group = bg2array(bound_group,prev_group=prev_group,next_group=next_group,is_test=is_test,grp_id=word_idx,config=letter_config,train=True,freqs=freqs)
encoding_cache[group_type] = encoded_group
all_encoded_groups += encoded_group
data_y += segs2array(segmentation)
sys.stderr.write("o Finished encoding " + str(len(data_y)) + " chars (" + str(len(seg_table)) + " groups, " + str(len(encoding_cache)) + " group types)\n")
if non_ident_segs > 0:
with open("bug_rows.txt",'w') as f:
f.write("\n".join([str(r) for r in sorted([shuffle_mapping[x] for x in bug_rows])]) + "\n")
sys.stderr.write("i WARN: found " + str(non_ident_segs) + " rows in training data where left column characters not identical to right column characters\n")
sys.stderr.write(" Row numbers dumped to: bug_rows.txt\n")
sys.stderr.write(" " + str(non_ident_segs) + " rows were ignored in training\n\n")
data_y = np.array(data_y)
cat_labels = ['group_in_lex','current_letter', 'prev_prev_letter', 'prev_letter', 'next_letter', 'next_next_letter',
'mns4_coarse', 'mns3_coarse', 'mns2_coarse',
'mns1_coarse', 'pls1_coarse', 'pls2_coarse',
'pls3_coarse', 'pls4_coarse', "so_far_pos", "remaining_pos","prev_grp_pos","next_grp_pos",
"remaining_pos_mns1","remaining_pos_mns2",
"prev_grp_first", "prev_grp_last","next_grp_first","next_grp_last"]
num_labels = ['idx','len_bound_group',"current_vowel","prev_prev_vowel","prev_vowel","next_vowel","next_next_vowel",
"prev_grp_len","next_grp_len","freq_ratio"]
# Remove features switched off in .conf file
for label in self.conf["unused"]:
if label in cat_labels:
cat_labels.remove(label)
if label in num_labels:
num_labels.remove(label)
# Handle temporary ablations if specified in option -a
if ablations is not None:
if len(ablations) > 0 and ablations != "none":
abl_feats = ablations.split(",")
sys.stderr.write("o Ablating features:\n")
for feat in abl_feats:
found = False
if feat in cat_labels:
cat_labels.remove(feat)
found = True
elif feat in num_labels:
num_labels.remove(feat)
found = True
if found:
sys.stderr.write("\t"+feat+"\n")
else:
sys.stderr.write("\tERR: can't find ablation feature " + feat + "\n")
sys.exit()
data_x = pd.DataFrame(all_encoded_groups, columns=headers)
###
if dump_transformed_data:
data_x["resp"] = data_y
import csv
to_remove = ["is_test","grp_id"] # Columns to remove from transformed data dump
out_cols = [col for col in headers if col not in to_remove] + ["resp"] # Add the response column as 'resp'
data_x.iloc[data_x.index[data_x["is_test"] == 0]].to_csv("rftokenizer_train_featurized.tab",sep="\t",quotechar="",quoting=csv.QUOTE_NONE,encoding="utf8",index=False,columns=out_cols)
data_x.iloc[data_x.index[data_x["is_test"] == 1]].to_csv("rftokenizer_test_featurized.tab",sep="\t",quotechar="",quoting=csv.QUOTE_NONE,encoding="utf8",index=False,columns=out_cols)
# Dump raw test rows to compare gold solution
with io.open("rftokenizer_test_gold.tab","w",encoding="utf8") as gold:
gold.write("\n".join(test_rows) + "\n")
sys.stderr.write("o Wrote featurized train/test set and gold test to rftokenizer_*.tab\n")
sys.exit()
###
encoder = MultiColumnLabelEncoder(pd.Index(cat_labels))
data_x_enc = encoder.fit_transform(data_x)
if test_prop > 0:
sys.stderr.write("o Generating train/test split with test proportion "+str(test_prop)+"\n")
data_x_enc["boundary"] = data_y
strat_train_set = data_x_enc.iloc[data_x_enc.index[data_x_enc["is_test"] == 0]]
strat_test_set = data_x_enc.iloc[data_x_enc.index[data_x_enc["is_test"] == 1]]
cat_pipeline = Pipeline([
('selector', DataFrameSelector(cat_labels)),
])
num_pipeline = Pipeline([
('selector', DataFrameSelector(num_labels))
])
preparation_pipeline = FeatureUnion(transformer_list=[
("cat_pipeline", cat_pipeline),
("num_pipeline", num_pipeline),
])
sys.stderr.write("o Transforming data to numerical array\n")
train_x = preparation_pipeline.fit_transform(strat_train_set)
train_y = strat_train_set["boundary"]
train_y_bin = np.where(strat_train_set['boundary'] == 0, 0, 1)
if test_prop > 0:
test_x = preparation_pipeline.transform(strat_test_set)
test_y_bin = np.where(strat_test_set['boundary'] == 0, 0, 1)
bound_grp_idx = np.array(strat_test_set['grp_id'])
from sklearn.dummy import DummyClassifier
d = DummyClassifier(strategy="most_frequent")
d.fit(train_x,train_y_bin)
pred = d.predict(test_x)
print("o Majority baseline:")
print("\t" + str(accuracy_score(test_y_bin, pred)))
forest_clf = ExtraTreesClassifier(n_estimators=250, max_features=None, n_jobs=3, random_state=42)
if cross_val_test:
# Modify code to tune hyperparameters/use different estimators
from sklearn.model_selection import GridSearchCV
sys.stderr.write("o Running CV...\n")
params = {"n_estimators":[300,400,500],"max_features":["auto",None]}#,"class_weight":["balanced",None]}
grid = GridSearchCV(RandomForestClassifier(n_jobs=-1,random_state=42,warm_start=True),param_grid=params,refit=False)
grid.fit(train_x,train_y_bin)
print("\nGrid search results:\n" + 30 * "=")
for key in grid.cv_results_:
print(key + ": " + str(grid.cv_results_[key]))
print("\nBest parameters:\n" + 30 * "=")
print(grid.best_params_)
sys.exit()
sys.stderr.write("o Learning...\n")
forest_clf.fit(train_x, train_y_bin)
if test_prop > 0:
pred = forest_clf.predict(test_x)
j=-1
for i, row in strat_test_set.iterrows():
j+=1
if row["idx"] +1 == row["len_bound_group"]:
pred[j] = 0
print("o Binary clf accuracy:")
print("\t" + str(accuracy_score(test_y_bin, pred)))
group_results = defaultdict(lambda : 1)
for i in range(len(pred)):
grp = bound_grp_idx[i]
if test_y_bin[i] != pred[i]:
group_results[grp] = 0
correct = 0
total = 0
for grp in set(bound_grp_idx):
if group_results[grp] == 1:
correct +=1
total +=1
print("o Perfect bound group accuracy:")
print("\t" + str(float(correct)/total))
errs = defaultdict(int)
for i, word in enumerate(words):
if i in group_results:
if group_results[i] == 0:
errs[word] += 1
if output_errors:
print("o Writing prediction errors to errs.txt")
with io.open("errs.txt",'w',encoding="utf8") as f:
for err in errs:
f.write(err + "\t" + str(errs[err])+"\n")
if output_importances:
feature_names = cat_labels + num_labels
zipped = zip(feature_names, forest_clf.feature_importances_)
sorted_zip = sorted(zipped, key=lambda x: x[1], reverse=True)
print("o Feature importances:\n")
for name, importance in sorted_zip:
print(name, "=", importance)
else:
print("o Test proportion is 0%, skipping evaluation")
if dump_model:
joblib.dump((forest_clf, num_labels, cat_labels, encoder, preparation_pipeline, pos_lookup, freqs, conf_file_parser), self.lang + ".sm" + str(sys.version_info[0]), compress=3)
print("o Dumped trained model to " + self.lang + ".sm" + str(sys.version_info[0]))
def _test_honeybadger(N=4, f=1, seed=None):
sid = 'sidA'
# Generate threshold sig keys
sPK, sSKs = dealer(N, f + 1, seed=seed)
# Generate threshold enc keys
ePK, eSKs = tpke.dealer(N, f + 1)
rnd = random.Random(seed)
#print 'SEED:', seed
router_seed = rnd.random()
sends, recvs = simple_router(N, seed=router_seed)
B = N # 1 tx per node, per round
badgers = [None] * N
threads = [None] * N
input_queues = [Queue() for _ in range(N)] # to submit lists of txes
output_queues = [Queue() for _ in range(N)] # to read lists of txes
for i in range(N):
input_queues[i].put(['<[HBBFT Input %d]>' % i])
for i in range(N):
badgers[i] = HoneyBadgerBFT(sid,
i,
B,
N,
f,
sPK,
sSKs[i],
ePK,
eSKs[i],
sends[i],
recvs[i],
input_queues[i].get,
output_queues[i].put,
encode=repr,
decode=ast.literal_eval,
max_rounds=3)
threads[i] = gevent.spawn(badgers[i].run)
for i in range(N):
input_queues[i].put(['<[HBBFT Input %d]>' % (i + 10)])
for i in range(N):
input_queues[i].put(['<[HBBFT Input %d]>' % (i + 20)])
try:
# Wait for each badger to finish running
for i in range(N):
threads[i].get()
output_queues[i].put(StopIteration)
outs = [tuple(output_queues[i]) for i in range(N)]
# Consistency check
assert len(set(outs)) == 1
except KeyboardInterrupt:
gevent.killall(threads)
raise
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
validate_flags_or_throw(bert_config)
tf.gfile.MakeDirs(FLAGS.output_dir)
tokenizer = tokenization.FullTokenizer(
vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case
)
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project
)
is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
run_config = tf.contrib.tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=FLAGS.output_dir,
save_checkpoints_steps=FLAGS.save_checkpoints_steps,
tpu_config=tf.contrib.tpu.TPUConfig(
iterations_per_loop=FLAGS.iterations_per_loop,
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=is_per_host,
),
)
train_examples = None
num_train_steps = None
num_warmup_steps = None
if FLAGS.do_train:
train_examples = read_squad_examples(input_file=FLAGS.train_file, is_training=True)
num_train_steps = int(len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
# Pre-shuffle the input to avoid having to make a very large shuffle
# buffer in in the `input_fn`.
rng = random.Random(12345)
rng.shuffle(train_examples)
model_fn = model_fn_builder(
bert_config=bert_config,
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=FLAGS.learning_rate,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu,
)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = tf.contrib.tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
predict_batch_size=FLAGS.predict_batch_size,
)
if FLAGS.do_train:
# We write to a temporary file to avoid storing very large constant tensors
# in memory.
train_writer = FeatureWriter(
filename=os.path.join(FLAGS.output_dir, "train.tf_record"), is_training=True
)
convert_examples_to_features(
examples=train_examples,
tokenizer=tokenizer,
max_seq_length=FLAGS.max_seq_length,
doc_stride=FLAGS.doc_stride,
max_query_length=FLAGS.max_query_length,
is_training=True,
output_fn=train_writer.process_feature,
)
train_writer.close()
tf.logging.info("***** Running training *****")
tf.logging.info(" Num orig examples = %d", len(train_examples))
tf.logging.info(" Num split examples = %d", train_writer.num_features)
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
tf.logging.info(" Num steps = %d", num_train_steps)
del train_examples
train_input_fn = input_fn_builder(
input_file=train_writer.filename,
seq_length=FLAGS.max_seq_length,
is_training=True,
drop_remainder=True,
)
estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
if FLAGS.do_predict:
eval_examples = read_squad_examples(input_file=FLAGS.predict_file, is_training=False)
eval_writer = FeatureWriter(
filename=os.path.join(FLAGS.output_dir, "eval.tf_record"), is_training=False
)
eval_features = []
def append_feature(feature):
eval_features.append(feature)
eval_writer.process_feature(feature)
convert_examples_to_features(
examples=eval_examples,
tokenizer=tokenizer,
max_seq_length=FLAGS.max_seq_length,
doc_stride=FLAGS.doc_stride,
max_query_length=FLAGS.max_query_length,
is_training=False,
output_fn=append_feature,
)
eval_writer.close()
tf.logging.info("***** Running predictions *****")
tf.logging.info(" Num orig examples = %d", len(eval_examples))
tf.logging.info(" Num split examples = %d", len(eval_features))
tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size)
all_results = []
predict_input_fn = input_fn_builder(
input_file=eval_writer.filename,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=False,
)
# If running eval on the TPU, you will need to specify the number of
# steps.
all_results = []
for result in estimator.predict(predict_input_fn, yield_single_examples=True):
if len(all_results) % 1000 == 0:
tf.logging.info("Processing example: %d" % (len(all_results)))
unique_id = int(result["unique_ids"])
start_logits = [float(x) for x in result["start_logits"].flat]
end_logits = [float(x) for x in result["end_logits"].flat]
all_results.append(
RawResult(unique_id=unique_id, start_logits=start_logits, end_logits=end_logits)
)
output_prediction_file = os.path.join(FLAGS.output_dir, "predictions.json")
output_nbest_file = os.path.join(FLAGS.output_dir, "nbest_predictions.json")
output_null_log_odds_file = os.path.join(FLAGS.output_dir, "null_odds.json")
write_predictions(
eval_examples,
eval_features,
all_results,
FLAGS.n_best_size,
FLAGS.max_answer_length,
FLAGS.do_lower_case,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
)
def start(self):
rand = random.Random()
self.__v = rand.choice([15])
self.__ang = 0 # TODO: random good angle
def __init__(self,
args=None,
topology=None,
results=None,
seed=None,
load=None,
policy=None,
id_simulation=None):
if args is not None and hasattr(args, 'mean_service_holding_time'):
self.mean_service_holding_time = args.mean_service_holding_time
else:
self.mean_service_holding_time = 86400.0 # service holding time in seconds (54000 sec = 15 h)
self.load = 0.0
self.mean_service_inter_arrival_time = 0.0
if args is not None and hasattr(args, 'load') and load is None:
self.set_load(load=args.load)
elif load is not None: # load through parameter has precedence over argument
self.set_load(load=load)
else:
self.set_load(load=50)
# num_seeds defines the number of seeds (simulations) to be run for each configuration
if args is not None and hasattr(args, "num_seeds"):
self.num_seeds = args.num_seeds
else:
self.num_seeds = 25
if args is not None and hasattr(args, "num_arrivals"):
self.num_arrivals = args.num_arrivals
else:
self.num_arrivals = 10000
if args is not None and hasattr(args, "k_paths"):
self.k_paths = args.k_paths
else:
self.k_paths = 5
if args is not None and hasattr(args, 'threads'):
self.threads = args.threads
else:
self.threads = 6
if args is not None and hasattr(args, 'topology_file'):
self.topology_file = args.topology_file
self.topology_name = args.topology_file.split('.')[0]
else:
self.topology_file = "nobel-us.xml" #"nobel-us.xml" #"test-topo.xml"
self.topology_name = 'nobel-us'
# self.topology_file = "simple" # "nobel-us.xml" #"test-topo.xml"
# self.topology_name = 'simple'
if args is not None and hasattr(args, "resource_units_per_link"):
self.resource_units_per_link = args.resource_units_per_link
else:
self.resource_units_per_link = 80
if args is not None and hasattr(args, "policy") and policy is None:
self.policy = args.policy
elif policy is not None:
self.policy = policy # parameter has precedence over argument
else:
self.policy = 'SP' # shortest path by default
if topology is not None:
self.topology = topology
if seed is not None:
self.seed = seed
self.rng = random.Random(seed)
else:
self.seed = 42
self.rng = random.Random(42)
if results is not None:
self.results = results
else:
self.results = [] # initiates with an empty local results vector
if id_simulation is not None:
self.id_simulation = id_simulation
else:
self.id_simulation = 0
self.track_stats_every = 100 # frequency at which results are saved
self.plot_tracked_stats_every = 1000 # frequency at which results are plotted
self.tracked_results = {}
self.tracked_statistics = [
'request_blocking_ratio', 'average_link_usage'
]
for obs in self.tracked_statistics:
self.tracked_results[obs] = []
self.events = [] # event queue
self._processed_arrivals = 0
self._rejected_services = 0
self.current_time = 0.0
self.output_folder = 'data'
self.plot_formats = [
'pdf'
] # you can configure this to other formats such as PNG, SVG
def test_multiple_channel_states(
chain_state,
token_network_state,
our_address,
):
open_block_number = 10
pseudo_random_generator = random.Random()
pkey, address = factories.make_privkey_address()
amount = 30
our_balance = amount + 50
channel_state = factories.make_channel(
our_balance=our_balance,
our_address=our_address,
partner_balance=our_balance,
partner_address=address,
token_network_identifier=token_network_state.address,
)
payment_network_identifier = factories.make_payment_network_identifier()
channel_new_state_change = ContractReceiveChannelNew(
factories.make_transaction_hash(),
token_network_state.address,
channel_state,
)
channel_new_iteration = token_network.state_transition(
payment_network_identifier,
token_network_state,
channel_new_state_change,
pseudo_random_generator,
open_block_number,
)
lock_amount = 30
lock_expiration = 20
lock_secret = sha3(b'test_end_state')
lock_secrethash = sha3(lock_secret)
lock = HashTimeLockState(
lock_amount,
lock_expiration,
lock_secrethash,
)
mediated_transfer = make_receive_transfer_mediated(
channel_state=channel_state,
privkey=pkey,
nonce=1,
transferred_amount=0,
lock=lock,
)
from_route = factories.route_from_channel(channel_state)
init_target = ActionInitTarget(
from_route,
mediated_transfer,
)
node.state_transition(chain_state, init_target)
closed_block_number = open_block_number + 10
channel_close_state_change = ContractReceiveChannelClosed(
factories.make_transaction_hash(),
channel_state.partner_state.address,
token_network_state.address,
channel_state.identifier,
closed_block_number,
)
channel_closed_iteration = token_network.state_transition(
payment_network_identifier,
channel_new_iteration.new_state,
channel_close_state_change,
pseudo_random_generator,
closed_block_number,
)
settle_block_number = closed_block_number + channel_state.settle_timeout + 1
channel_settled_state_change = ContractReceiveChannelSettled(
factories.make_transaction_hash(),
token_network_state.address,
channel_state.identifier,
settle_block_number,
)
channel_settled_iteration = token_network.state_transition(
payment_network_identifier,
channel_closed_iteration.new_state,
channel_settled_state_change,
pseudo_random_generator,
closed_block_number,
)
token_network_state_after_settle = channel_settled_iteration.new_state
ids_to_channels = token_network_state_after_settle.channelidentifiers_to_channels
assert len(ids_to_channels) == 1
assert channel_state.identifier in ids_to_channels
# Create new channel while the previous one is pending unlock
new_channel_state = factories.make_channel(
our_balance=our_balance,
partner_balance=our_balance,
partner_address=address,
)
channel_new_state_change = ContractReceiveChannelNew(
factories.make_transaction_hash(),
token_network_state.address,
new_channel_state,
)
channel_new_iteration = token_network.state_transition(
payment_network_identifier,
token_network_state,
channel_new_state_change,
pseudo_random_generator,
open_block_number,
)
token_network_state_after_new_open = channel_new_iteration.new_state
ids_to_channels = token_network_state_after_new_open.channelidentifiers_to_channels
assert len(ids_to_channels) == 2
assert channel_state.identifier in ids_to_channels
def setUp(self):
self.random = random.Random(42)
def test_channel_settle_must_properly_cleanup():
open_block_number = 10
pseudo_random_generator = random.Random()
token_network_id = factories.make_address()
token_id = factories.make_address()
token_network_state = TokenNetworkState(token_network_id, token_id)
payment_network_identifier = factories.make_payment_network_identifier()
amount = 30
our_balance = amount + 50
channel_state = factories.make_channel(our_balance=our_balance)
channel_new_state_change = ContractReceiveChannelNew(
factories.make_transaction_hash(),
token_network_id,
channel_state,
)
channel_new_iteration = token_network.state_transition(
payment_network_identifier,
token_network_state,
channel_new_state_change,
pseudo_random_generator,
open_block_number,
)
closed_block_number = open_block_number + 10
channel_close_state_change = ContractReceiveChannelClosed(
factories.make_transaction_hash(),
channel_state.partner_state.address,
token_network_id,
channel_state.identifier,
closed_block_number,
)
channel_closed_iteration = token_network.state_transition(
payment_network_identifier,
channel_new_iteration.new_state,
channel_close_state_change,
pseudo_random_generator,
closed_block_number,
)
settle_block_number = closed_block_number + channel_state.settle_timeout + 1
channel_settled_state_change = ContractReceiveChannelSettled(
factories.make_transaction_hash(),
token_network_id,
channel_state.identifier,
settle_block_number,
)
channel_settled_iteration = token_network.state_transition(
payment_network_identifier,
channel_closed_iteration.new_state,
channel_settled_state_change,
pseudo_random_generator,
closed_block_number,
)
token_network_state_after_settle = channel_settled_iteration.new_state
ids_to_channels = token_network_state_after_settle.channelidentifiers_to_channels
assert channel_state.identifier not in ids_to_channels
def prepare_file_path_queue(self):
"""Generate more file paths to process. Result are saved in _file_path_queue."""
self._parsing_start_time = time.perf_counter()
# If the file path is already being processed, or if a file was
# processed recently, wait until the next batch
file_paths_in_progress = self._processors.keys()
now = timezone.utcnow()
# Sort the file paths by the parsing order mode
list_mode = conf.get("scheduler", "file_parsing_sort_mode")
files_with_mtime = {}
file_paths = []
is_mtime_mode = list_mode == "modified_time"
file_paths_recently_processed = []
for file_path in self._file_paths:
if is_mtime_mode:
try:
files_with_mtime[file_path] = os.path.getmtime(file_path)
except FileNotFoundError:
self.log.warning("Skipping processing of missing file: %s",
file_path)
continue
file_modified_time = timezone.make_aware(
datetime.fromtimestamp(files_with_mtime[file_path]))
else:
file_paths.append(file_path)
file_modified_time = None
# Find file paths that were recently processed to exclude them
# from being added to file_path_queue
# unless they were modified recently and parsing mode is "modified_time"
# in which case we don't honor "self._file_process_interval" (min_file_process_interval)
last_finish_time = self.get_last_finish_time(file_path)
if (last_finish_time is not None
and (now - last_finish_time).total_seconds() <
self._file_process_interval
and not (is_mtime_mode and file_modified_time and
(file_modified_time > last_finish_time))):
file_paths_recently_processed.append(file_path)
# Sort file paths via last modified time
if is_mtime_mode:
file_paths = sorted(files_with_mtime,
key=files_with_mtime.get,
reverse=True)
elif list_mode == "alphabetical":
file_paths = sorted(file_paths)
elif list_mode == "random_seeded_by_host":
# Shuffle the list seeded by hostname so multiple schedulers can work on different
# set of files. Since we set the seed, the sort order will remain same per host
random.Random(get_hostname()).shuffle(file_paths)
files_paths_at_run_limit = [
file_path for file_path, stat in self._file_stats.items()
if stat.run_count == self._max_runs
]
file_paths_to_exclude = set(file_paths_in_progress).union(
file_paths_recently_processed, files_paths_at_run_limit)
# Do not convert the following list to set as set does not preserve the order
# and we need to maintain the order of file_paths for `[scheduler] file_parsing_sort_mode`
files_paths_to_queue = [
file_path for file_path in file_paths
if file_path not in file_paths_to_exclude
]
for file_path, processor in self._processors.items():
self.log.debug(
"File path %s is still being processed (started: %s)",
processor.file_path,
processor.start_time.isoformat(),
)
self.log.debug("Queuing the following files for processing:\n\t%s",
"\n\t".join(files_paths_to_queue))
for file_path in files_paths_to_queue:
if file_path not in self._file_stats:
self._file_stats[file_path] = DagFileStat(
num_dags=0,
import_errors=0,
last_finish_time=None,
last_duration=None,
run_count=0)
self._file_path_queue.extend(files_paths_to_queue)
def __init__(self, playable_encounter, encounter_display):
self.rnd = random.Random()
self.ended = False
self.__inital_done = False
self.encounter = playable_encounter
self.display = encounter_display
def start(father="data/", changeRate=0.1, filename='twitter.txt'):
fx = open(father + filename)
fc = open(father + 'changey.txt', 'w')
fy = open(father + 'y2.txt', 'w')
biggestNode = 1
nowIter = 0
nodeSet = set()
nodeSet2 = set()
#1.-
for line in fx.readlines():
lineArr = re.split(' |,|\t', line.strip())
lenth = len(lineArr)
if (lenth < 2):
break
fc.write('%s %s\n' % (lineArr[0], lineArr[1]))
nodeSet.add(int(lineArr[0]))
nodeSet.add(int(lineArr[1]))
nodeSet2.add(int(lineArr[0]))
nodeSet2.add(int(lineArr[1]))
nowIter += 1
biggestNode = max(nodeSet2)
print "biggestNode=", biggestNode
#2.
fc.close()
fc = open(father + 'changey.txt', 'r')
removeSet = set()
lenSet = int(len(nodeSet))
nowIter = 0
nodeSet = list(nodeSet)
random.Random(0).shuffle(nodeSet)
#delete randomly
for nowIter in range(int(changeRate * lenSet)):
node = random.Random().choice(nodeSet)
removeSet.add(node)
nodeSet2.remove(node)
nodeSet.remove(node)
print "len(removeSet)=", len(removeSet)
#
for line in fc.readlines():
lineArr = re.split(' |,|\t', line.strip()) #
lenth = len(lineArr)
if (lenth < 2): #
break
if int(lineArr[0]) not in removeSet and int(
lineArr[1]) not in removeSet:
fy.write('a%s a%s\n' % (lineArr[0], lineArr[1]))
#print "removeSet=",removeSet
fremove = open(father + 'remove.txt', 'w')
for ra in removeSet:
fremove.write('%s ' % ra)
fremove.close()
#3.
#biggestNode=int(5000)
newNode = int(biggestNode) #
alllen, du, xDict = cal.start(father + filename)
#alllen=11765;du=947;
for j in range(int(changeRate * alllen)):
newNode += 1 #
i = 0
nowIter = 0
nodeSet2 = list(nodeSet2)
random.Random(0).shuffle(nodeSet2)
addset = set()
while (i < du):
nodeY = random.Random().choice(nodeSet2)
if (nodeY not in addset):
addset.add(nodeY)
fy.write('a%s a%s\n' % (newNode, nodeY))
#print "i=",i,":",newNode,"+",nodeY," "
i += 1
ff = open(father + 'connectY2.txt', 'w')
for node in nodeSet2:
ff.write('%s a%s\n' % (node, node))
ff.close()
fy.close()
fc.close()
fx.close()
return xDict
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
from nltk.cluster import KMeansClusterer
import nltk
import json
import random
from nltk.cluster.util import cosine_distance
config = json.load(open('../config.json', 'r'))
INPUT_DIR = config['INPUT_DIR']
OUTPUT_DIR = config['OUTPUT_DIR']
TWEET_DIR = config['TWEET_DIR']
NUM_CLUSTERS = config['NUM_CLUSTERS']
RNG = random.Random()
RNG.seed(config['SEED'])
events = open(INPUT_DIR + 'event_names.txt', 'r').read().splitlines()
print('loading...')
# use a joint sample of embeddings from each event to determine the cluster centroids
def get_samples():
tweet_embeds = ''
for event in events:
embeds = np.load(TWEET_DIR + event + '/' + event +
'_embeddings_partisan.npy')
indices = np.load(TWEET_DIR + event + '/' + event +
'_indices_among_embeddings_for_getting_topics.npy')
embeds = embeds[indices, :]
if len(tweet_embeds) == 0:
import random
from eth_keys import keys
from hexbytes import HexBytes
from web3.datastructures import MutableAttributeDict
from monitor.blocks import get_canonicalized_block, calculate_block_signature
random_generator = random.Random(0)
def random_hash():
return bytes(random_generator.randint(0, 255) for _ in range(32))
def random_address():
return bytes(random_generator.randint(0, 255) for _ in range(20))
def random_private_key():
return keys.PrivateKey(random_hash())
def random_step():
return random_generator.randint(0, 2**32)
def make_block(*,
block_hash=None,
parent_hash=None,
proposer_privkey=None,
step=None):
if proposer_privkey is None:
def Train(self):
fixedRandomSeed = 0
trainToValidationChance = 0.2
includeEvaluationInValidation = True
stepMultiplier = 1.0
stepCount = 1000
showInputs = False
augmentationLevel = 0
detNMSThresh = 0.35
rpnNMSThresh = 0.55
trainDir = os.path.join(os.curdir, self.__mParams["train_dir"])
evalDir = os.path.join(os.curdir, self.__mParams["eval_dir"])
inModelPath = os.path.join(os.curdir, self.__mParams["input_model"])
os.makedirs(name=self.__mParams["output_dir"], exist_ok=True)
outModelPath = os.path.join(self.__mParams["output_dir"],
self.__mParams["model_name"] + ".h5")
blankInput = True
if "eval_dir" in self.__mParams:
evalDir = os.path.join(os.curdir, self.__mParams["eval_dir"])
if "image_size" in self.__mParams:
maxdim = self.__mParams["image_size"]
if "train_to_val_ratio" in self.__mParams:
trainToValidationChance = self.__mParams["train_to_val_ratio"]
if "step_num" in self.__mParams:
stepCount = self.__mParams["step_num"]
if "show_inputs" in self.__mParams:
showInputs = self.__mParams["show_inputs"]
if "random_augmentation_level" in self.__mParams:
augmentationLevel = self.__mParams["random_augmentation_level"]
if "detection_nms_threshold" in self.__mParams:
detNMSThresh = self.__mParams["detection_nms_threshold"]
if "rpn_nms_threshold" in self.__mParams:
rpnNMSThresh = self.__mParams["rpn_nms_threshold"]
rnd = random.Random()
rnd.seed(fixedRandomSeed)
trainImagesAndMasks = {}
validationImagesAndMasks = {}
# iterate through train set
imagesDir = os.path.join(trainDir, "images")
masksDir = os.path.join(trainDir, "masks")
# adding evaluation data into validation
if includeEvaluationInValidation and evalDir is not None:
# iterate through test set
imagesValDir = os.path.join(evalDir, "images")
masksValDir = os.path.join(evalDir, "masks")
imageValFileList = [
f for f in os.listdir(imagesValDir)
if os.path.isfile(os.path.join(imagesValDir, f))
]
for imageFile in imageValFileList:
baseName = os.path.splitext(os.path.basename(imageFile))[0]
imagePath = os.path.join(imagesValDir, imageFile)
if os.path.exists(os.path.join(masksValDir,
baseName + ".png")):
maskPath = os.path.join(masksValDir, baseName + ".png")
elif os.path.exists(
os.path.join(masksValDir, baseName + ".tif")):
maskPath = os.path.join(masksValDir, baseName + ".tif")
elif os.path.exists(
os.path.join(masksValDir, baseName + ".tiff")):
maskPath = os.path.join(masksValDir, baseName + ".tiff")
else:
sys.exit(
"The image " + imageFile +
" does not have a corresponding mask file ending with png, tif or tiff"
)
if not os.path.isfile(imagePath) or not os.path.isfile(
maskPath):
continue
validationImagesAndMasks[imagePath] = maskPath
imageFileList = [
f for f in os.listdir(imagesDir)
if os.path.isfile(os.path.join(imagesDir, f))
]
for imageFile in imageFileList:
baseName = os.path.splitext(os.path.basename(imageFile))[0]
imagePath = os.path.join(imagesDir, imageFile)
if os.path.exists(os.path.join(masksDir, baseName + ".png")):
maskPath = os.path.join(masksDir, baseName + ".png")
elif os.path.exists(os.path.join(masksDir, baseName + ".tif")):
maskPath = os.path.join(masksDir, baseName + ".tif")
elif os.path.exists(os.path.join(masksDir,
baseName + ".tiff")):
maskPath = os.path.join(masksDir, baseName + ".tiff")
else:
sys.exit(
"The image " + imageFile +
" does not have a corresponding mask file ending with png, tif or tiff"
)
if not os.path.isfile(imagePath) or not os.path.isfile(
maskPath):
continue
trainImagesAndMasks[imagePath] = maskPath
# splitting train data into train and validation
else:
imageFileList = [
f for f in os.listdir(imagesDir)
if os.path.isfile(os.path.join(imagesDir, f))
]
for imageFile in imageFileList:
baseName = os.path.splitext(os.path.basename(imageFile))[0]
imagePath = os.path.join(imagesDir, imageFile)
if os.path.exists(os.path.join(masksDir, baseName + ".png")):
maskPath = os.path.join(masksDir, baseName + ".png")
elif os.path.exists(os.path.join(masksDir, baseName + ".tif")):
maskPath = os.path.join(masksDir, baseName + ".tif")
elif os.path.exists(os.path.join(masksDir,
baseName + ".tiff")):
maskPath = os.path.join(masksDir, baseName + ".tiff")
else:
sys.exit(
"The image " + imageFile +
" does not have a corresponding mask file ending with png, tif or tiff"
)
if not os.path.isfile(imagePath) or not os.path.isfile(
maskPath):
continue
if rnd.random() > trainToValidationChance:
trainImagesAndMasks[imagePath] = maskPath
else:
validationImagesAndMasks[imagePath] = maskPath
if len(trainImagesAndMasks) < 1:
sys.exit("Empty train image list")
#just to be non-empty
if len(validationImagesAndMasks) < 1:
for key, value in trainImagesAndMasks.items():
validationImagesAndMasks[key] = value
break
# Training dataset
dataset_train = mask_rcnn_additional.NucleiDataset()
dataset_train.initialize(pImagesAndMasks=trainImagesAndMasks,
pAugmentationLevel=augmentationLevel)
dataset_train.prepare()
# Validation dataset
dataset_val = mask_rcnn_additional.NucleiDataset()
dataset_val.initialize(pImagesAndMasks=validationImagesAndMasks,
pAugmentationLevel=0)
dataset_val.prepare()
print("training images (with augmentation):", dataset_train.num_images)
print("validation images (with augmentation):", dataset_val.num_images)
config = mask_rcnn_additional.NucleiConfig()
config.IMAGE_MAX_DIM = maxdim
config.IMAGE_MIN_DIM = maxdim
config.STEPS_PER_EPOCH = stepCount
if stepMultiplier is not None:
steps = int(float(dataset_train.num_images) * stepMultiplier)
config.STEPS_PER_EPOCH = steps
config.VALIDATION_STEPS = dataset_val.num_images
config.DETECTION_NMS_THRESHOLD = detNMSThresh
config.RPN_NMS_THRESHOLD = rpnNMSThresh
config.MAX_GT_INSTANCES = 512
config.BATCH_SIZE = 5000
config.__init__()
# Create model in training mode
mdl = mrcnn_model.MaskRCNN(mode="training",
config=config,
model_dir=os.path.dirname(outModelPath))
if blankInput:
mdl.load_weights(inModelPath,
by_name=True,
exclude=[
"mrcnn_class_logits", "mrcnn_bbox_fc",
"mrcnn_bbox", "mrcnn_mask"
])
else:
mdl.load_weights(inModelPath, by_name=True)
allcount = 0
logdir = "logs/scalars/" + self.__mParams["model_name"]
for epochgroup in self.__mParams["epoch_groups"]:
epochs = int(epochgroup["epochs"])
if epochs < 1:
continue
allcount += epochs
mdl.train(dataset_train,
dataset_val,
learning_rate=float(epochgroup["learning_rate"]),
epochs=allcount,
layers=epochgroup["layers"])
mdl.keras_model.save_weights(outModelPath)
def configuration_model_label_z_score_mixing_matrix(Data,
runs=1000,
label="type",
shuffle_label=False,
force_simple_graph=False,
seed_config_mat=None,
seed_label=None):
r"""Creates a contact matrix based on the configuration model
Creates a contact matrix with z-scores based on the chosen attribute. You can assume randomized attributes and/
or randomized degrees in the null model.
Parameters
----------
Data: Data
Data Object that contains Tij- and Metadata for a data set.
runs: int
The amount of times the function should be executed. It's a heuristic approach, so the more the runs the
better might be the result
label: str
A string that tells the function for which attribute the contact matrix should be made.
shuffle_label: bool
Gives the option to extend the null model by randomizing the node attributes.
force_simple_graph: bool
Deletes parallel- and selfedges that can occur by using the networkx "configuration_model"-function if True.
seed_config_mat : List, default None
Allows to create reproducible configuration models for a reproducible output. This parameters is basically
just for applying tests.
seed_label : list, default None
Allows to create reproducible "randomized" labels for a reproducible output. This parameters is basically
just for applying tests.
Returns
-------
contact_matrix : list
A matrixlike List of lists that contains the z-scores for a given attribute
References
----------
.. [1] Génois, Mathieu & Zens, Maria & Lechner, Clemens & Rammstedt, Beatrice & Strohmaier, Markus. (2019).
Building connections: How scientists meet each other during a conference.
Examples
---------
>>> contact_matrix = configuration_model_label_z_score_mixing_matrix(test_network, test_df, runs=1000,
>>> label="Age", shuffle_label=True,
>>> force_simple_graph=True)
>>> print(contact_matrix)
[[1.0320936930842797, -0.7717976357301974, -0.5],
[-0.7717976357301974, -0.0667601413575786, -1.2927763604862383],
[-0.5, -1.2927763604862383, 2.632147318581194]]
See Also
---------
face2face.statistics.null_modell.shuffle_label_z_score_mixing_matrix
"""
network = create_network_from_data(Data, replace_attr=True, label=label)
mapping = mapping_function(Data, label)
data_mixing_matrix = nx.assortativity.attribute_mixing_matrix(
network, label, mapping=mapping)
degree_sequence = [v[1] for v in network.degree]
type_sequence = [network.nodes[n][label] for n in network.nodes]
matrices = []
matrices_abs = []
for _ in range(runs):
if seed_config_mat is None:
null_model = nx.configuration_model(degree_sequence)
else:
null_model = nx.configuration_model(degree_sequence,
seed=seed_config_mat[_])
if force_simple_graph:
null_model = to_simple_graph(null_model)
if shuffle_label:
if seed_label is None:
np.random.shuffle(type_sequence)
else:
random.Random(seed_label[_]).shuffle(type_sequence)
for n, t in zip(null_model.nodes, type_sequence):
null_model.nodes[n][label] = t
matrices.append(
nx.assortativity.attribute_mixing_matrix(null_model,
label,
mapping=mapping))
matrices_abs.append(
nx.assortativity.attribute_mixing_matrix(null_model,
label,
mapping=mapping,
normalized=False))
return (data_mixing_matrix - np.array(matrices).mean(axis=0))/np.array(matrices).std(axis=0), matrices, \
data_mixing_matrix, matrices_abs
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Random utils"""
import random
import numpy as np
rng = random.Random()
nprng = np.random.RandomState()
def set_seeds(seed):
"""Sets the seeds of both random number generators used by Foolbox.
Parameters
----------
seed : int
The seed for both random number generators.
"""
rng.seed(seed)
nprng.seed(seed)
#ATS:test(SELF, label="PolyClipper 2D (polygon) tests")
import unittest
from math import *
import time
from PolyhedronFileUtilities import *
from Spheral2d import *
from SpheralTestUtilities import fuzzyEqual
# Create a global random number generator.
import random
rangen = random.Random()
#-------------------------------------------------------------------------------
# Make a square
# 3 2
# |----|
# | |
# |----|
# 0 1
square_points = vector_of_Vector()
for coords in [(0, 0), (10, 0), (10, 10), (0, 10)]:
square_points.append(Vector(*coords))
#-------------------------------------------------------------------------------
# Make a non-convex notched thingy.
# 6 5 3 2
# |------------\ /-----------|
# | \ / |
