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MillionIntegrals/vel | vel/util/network.py | convolutional_layer_series | def convolutional_layer_series(initial_size, layer_sequence):
""" Execute a series of convolutional layer transformations to the size number """
size = initial_size
for filter_size, padding, stride in layer_sequence:
size = convolution_size_equation(size, filter_size, padding, stride)
return s... | python | def convolutional_layer_series(initial_size, layer_sequence):
""" Execute a series of convolutional layer transformations to the size number """
size = initial_size
for filter_size, padding, stride in layer_sequence:
size = convolution_size_equation(size, filter_size, padding, stride)
return s... | [
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MillionIntegrals/vel | vel/api/model.py | Model.train | def train(self, mode=True):
r"""
Sets the module in training mode.
This has any effect only on certain modules. See documentations of
particular modules for details of their behaviors in training/evaluation
mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`,
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Sets the module in training mode.
This has any effect only on certain modules. See documentations of
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mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`,
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MillionIntegrals/vel | vel/api/model.py | Model.summary | def summary(self, input_size=None, hashsummary=False):
""" Print a model summary """
if input_size is None:
print(self)
print("-" * 120)
number = sum(p.numel() for p in self.model.parameters())
print("Number of model parameters: {:,}".format(number))
... | python | def summary(self, input_size=None, hashsummary=False):
""" Print a model summary """
if input_size is None:
print(self)
print("-" * 120)
number = sum(p.numel() for p in self.model.parameters())
print("Number of model parameters: {:,}".format(number))
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MillionIntegrals/vel | vel/api/model.py | Model.hashsummary | def hashsummary(self):
""" Print a model summary - checksums of each layer parameters """
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result.extend(hashlib.sha256(x.detach().cpu().numpy().tobytes()).hexdigest() for x in child.parameters())
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""" Print a model summary - checksums of each layer parameters """
children = list(self.children())
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result.extend(hashlib.sha256(x.detach().cpu().numpy().tobytes()).hexdigest() for x in child.parameters())
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MillionIntegrals/vel | vel/api/model.py | RnnLinearBackboneModel.zero_state | def zero_state(self, batch_size):
""" Initial state of the network """
return torch.zeros(batch_size, self.state_dim, dtype=torch.float32) | python | def zero_state(self, batch_size):
""" Initial state of the network """
return torch.zeros(batch_size, self.state_dim, dtype=torch.float32) | [
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MillionIntegrals/vel | vel/api/model.py | SupervisedModel.loss | def loss(self, x_data, y_true):
""" Forward propagate network and return a value of loss function """
y_pred = self(x_data)
return y_pred, self.loss_value(x_data, y_true, y_pred) | python | def loss(self, x_data, y_true):
""" Forward propagate network and return a value of loss function """
y_pred = self(x_data)
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MillionIntegrals/vel | vel/models/vision/cifar_resnet_v2.py | ResNetV2.metrics | def metrics(self):
""" Set of metrics for this model """
from vel.metrics.loss_metric import Loss
from vel.metrics.accuracy import Accuracy
return [Loss(), Accuracy()] | python | def metrics(self):
""" Set of metrics for this model """
from vel.metrics.loss_metric import Loss
from vel.metrics.accuracy import Accuracy
return [Loss(), Accuracy()] | [
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MillionIntegrals/vel | vel/util/tensor_util.py | one_hot_encoding | def one_hot_encoding(input_tensor, num_labels):
""" One-hot encode labels from input """
xview = input_tensor.view(-1, 1).to(torch.long)
onehot = torch.zeros(xview.size(0), num_labels, device=input_tensor.device, dtype=torch.float)
onehot.scatter_(1, xview, 1)
return onehot.view(list(input_tensor.s... | python | def one_hot_encoding(input_tensor, num_labels):
""" One-hot encode labels from input """
xview = input_tensor.view(-1, 1).to(torch.long)
onehot = torch.zeros(xview.size(0), num_labels, device=input_tensor.device, dtype=torch.float)
onehot.scatter_(1, xview, 1)
return onehot.view(list(input_tensor.s... | [
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MillionIntegrals/vel | vel/util/tensor_util.py | merge_first_two_dims | def merge_first_two_dims(tensor):
""" Reshape tensor to merge first two dimensions """
shape = tensor.shape
batch_size = shape[0] * shape[1]
new_shape = tuple([batch_size] + list(shape[2:]))
return tensor.view(new_shape) | python | def merge_first_two_dims(tensor):
""" Reshape tensor to merge first two dimensions """
shape = tensor.shape
batch_size = shape[0] * shape[1]
new_shape = tuple([batch_size] + list(shape[2:]))
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MillionIntegrals/vel | vel/rl/vecenv/dummy.py | DummyVecEnvWrapper.instantiate | def instantiate(self, parallel_envs, seed=0, preset='default') -> VecEnv:
""" Create vectorized environments """
envs = DummyVecEnv([self._creation_function(i, seed, preset) for i in range(parallel_envs)])
if self.frame_history is not None:
envs = VecFrameStack(envs, self.frame_hist... | python | def instantiate(self, parallel_envs, seed=0, preset='default') -> VecEnv:
""" Create vectorized environments """
envs = DummyVecEnv([self._creation_function(i, seed, preset) for i in range(parallel_envs)])
if self.frame_history is not None:
envs = VecFrameStack(envs, self.frame_hist... | [
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MillionIntegrals/vel | vel/rl/vecenv/dummy.py | DummyVecEnvWrapper.instantiate_single | def instantiate_single(self, seed=0, preset='default'):
""" Create a new Env instance - single """
env = self.env.instantiate(seed=seed, serial_id=0, preset=preset)
if self.frame_history is not None:
env = FrameStack(env, self.frame_history)
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""" Create a new Env instance - single """
env = self.env.instantiate(seed=seed, serial_id=0, preset=preset)
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MillionIntegrals/vel | vel/rl/vecenv/dummy.py | DummyVecEnvWrapper._creation_function | def _creation_function(self, idx, seed, preset):
""" Helper function to create a proper closure around supplied values """
return lambda: self.env.instantiate(seed=seed, serial_id=idx, preset=preset) | python | def _creation_function(self, idx, seed, preset):
""" Helper function to create a proper closure around supplied values """
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MillionIntegrals/vel | vel/rl/models/stochastic_policy_model_separate.py | StochasticPolicyModelSeparate.policy | def policy(self, observations):
""" Calculate only action head for given state """
input_data = self.input_block(observations)
policy_base_output = self.policy_backbone(input_data)
policy_params = self.action_head(policy_base_output)
return policy_params | python | def policy(self, observations):
""" Calculate only action head for given state """
input_data = self.input_block(observations)
policy_base_output = self.policy_backbone(input_data)
policy_params = self.action_head(policy_base_output)
return policy_params | [
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MillionIntegrals/vel | vel/phase/cycle.py | CycleCallback._init_cycle_dict | def _init_cycle_dict(self):
""" Populate a cycle dict """
dict_arr = np.zeros(self.epochs, dtype=int)
length_arr = np.zeros(self.epochs, dtype=int)
start_arr = np.zeros(self.epochs, dtype=int)
c_len = self.cycle_len
idx = 0
for i in range(self.cycles):
... | python | def _init_cycle_dict(self):
""" Populate a cycle dict """
dict_arr = np.zeros(self.epochs, dtype=int)
length_arr = np.zeros(self.epochs, dtype=int)
start_arr = np.zeros(self.epochs, dtype=int)
c_len = self.cycle_len
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MillionIntegrals/vel | vel/phase/cycle.py | CycleCallback.on_batch_begin | def on_batch_begin(self, batch_info: BatchInfo):
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cycle_length = self.cycle_lengths[batch_info.local_epoch_number - 1]
cycle_start = self.cycle_starts[batch_info.local_epoch_number - 1]
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""" Set proper learning rate """
cycle_length = self.cycle_lengths[batch_info.local_epoch_number - 1]
cycle_start = self.cycle_starts[batch_info.local_epoch_number - 1]
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MillionIntegrals/vel | vel/phase/cycle.py | CycleCallback.set_lr | def set_lr(self, lr):
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param_group['lr'] = group_lr
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... | python | def set_lr(self, lr):
""" Set a learning rate for the optimizer """
if isinstance(lr, list):
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param_group['lr'] = group_lr
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MillionIntegrals/vel | vel/internals/parser.py | Variable.parameter_constructor | def parameter_constructor(cls, loader, node):
""" Construct variable instance from yaml node """
value = loader.construct_scalar(node)
if isinstance(value, str):
if '=' in value:
(varname, varvalue) = Parser.parse_equality(value)
return cls(varname, v... | python | def parameter_constructor(cls, loader, node):
""" Construct variable instance from yaml node """
value = loader.construct_scalar(node)
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MillionIntegrals/vel | vel/rl/buffers/prioritized_circular_replay_buffer.py | PrioritizedCircularReplayBuffer._get_transitions | def _get_transitions(self, probs, indexes, tree_idxs, batch_info, forward_steps=1, discount_factor=1.0):
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MillionIntegrals/vel | vel/rl/api/env_roller.py | EnvRollerBase.rollout | def rollout(self, batch_info: BatchInfo, model: Model, number_of_steps: int) -> Rollout:
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MillionIntegrals/vel | vel/rl/reinforcers/buffered_mixed_policy_iteration_reinforcer.py | BufferedMixedPolicyIterationReinforcer.train_epoch | def train_epoch(self, epoch_info: EpochInfo, interactive=True):
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MillionIntegrals/vel | vel/rl/reinforcers/buffered_mixed_policy_iteration_reinforcer.py | BufferedMixedPolicyIterationReinforcer.train_batch | def train_batch(self, batch_info: BatchInfo):
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MillionIntegrals/vel | vel/rl/reinforcers/buffered_mixed_policy_iteration_reinforcer.py | BufferedMixedPolicyIterationReinforcer.on_policy_train_batch | def on_policy_train_batch(self, batch_info: BatchInfo):
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MillionIntegrals/vel | vel/rl/reinforcers/buffered_mixed_policy_iteration_reinforcer.py | BufferedMixedPolicyIterationReinforcer.off_policy_train_batch | def off_policy_train_batch(self, batch_info: BatchInfo):
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MillionIntegrals/vel | vel/storage/strategy/classic_checkpoint_strategy.py | ClassicCheckpointStrategy.should_store_best_checkpoint | def should_store_best_checkpoint(self, epoch_idx, metrics) -> bool:
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MillionIntegrals/vel | vel/sources/nlp/imdb.py | create | def create(model_config, batch_size, vectors=None):
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path = model_config.data_dir('imdb')
text_field = data.Field(lower=True, tokenize='spacy', batch_first=True)
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train_source, test_source = IMDBCached.splits(
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""" Create an IMDB dataset """
path = model_config.data_dir('imdb')
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MillionIntegrals/vel | vel/commands/augvis_command.py | AugmentationVisualizationCommand.run | def run(self):
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MillionIntegrals/vel | vel/rl/env/classic_control.py | env_maker | def env_maker(environment_id, seed, serial_id, monitor=False, allow_early_resets=False):
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env = gym.make(environment_id)
env.seed(seed + serial_id)
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""" Create a classic control environment with basic set of wrappers """
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MillionIntegrals/vel | vel/models/imagenet/resnet34.py | Resnet34.freeze | def freeze(self, number=None):
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MillionIntegrals/vel | vel/util/better.py | better | def better(old_value, new_value, mode):
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MillionIntegrals/vel | vel/rl/discount_bootstrap.py | discount_bootstrap | def discount_bootstrap(rewards_buffer, dones_buffer, final_values, discount_factor, number_of_steps):
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MillionIntegrals/vel | vel/internals/model_config.py | ModelConfig.find_project_directory | def find_project_directory(start_path) -> str:
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MillionIntegrals/vel | vel/internals/model_config.py | ModelConfig.from_file | def from_file(cls, filename: str, run_number: int, continue_training: bool = False, seed: int = None,
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MillionIntegrals/vel | vel/internals/model_config.py | ModelConfig.run_command | def run_command(self, command_name, varargs):
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douban/libmc | misc/runbench.py | benchmark_method | def benchmark_method(f):
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liampauling/betfair | betfairlightweight/resources/baseresource.py | BaseResource.strip_datetime | def strip_datetime(value):
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liampauling/betfair | betfairlightweight/baseclient.py | BaseClient.set_session_token | def set_session_token(self, session_token):
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Sets session token and new login time.
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Sets session token and new login time.
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liampauling/betfair | betfairlightweight/baseclient.py | BaseClient.get_password | def get_password(self):
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liampauling/betfair | betfairlightweight/baseclient.py | BaseClient.get_app_key | def get_app_key(self):
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if self.app_key is None:
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liampauling/betfair | betfairlightweight/baseclient.py | BaseClient.session_expired | def session_expired(self):
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liampauling/betfair | betfairlightweight/utils.py | check_status_code | def check_status_code(response, codes=None):
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liampauling/betfair | betfairlightweight/endpoints/betting.py | Betting.list_runner_book | def list_runner_book(self, market_id, selection_id, handicap=None, price_projection=None, order_projection=None,
match_projection=None, include_overall_position=None, partition_matched_by_strategy_ref=None,
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liampauling/betfair | betfairlightweight/endpoints/betting.py | Betting.list_current_orders | def list_current_orders(self, bet_ids=None, market_ids=None, order_projection=None, customer_order_refs=None,
customer_strategy_refs=None, date_range=time_range(), order_by=None, sort_dir=None,
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liampauling/betfair | betfairlightweight/endpoints/betting.py | Betting.list_cleared_orders | def list_cleared_orders(self, bet_status='SETTLED', event_type_ids=None, event_ids=None, market_ids=None,
runner_ids=None, bet_ids=None, customer_order_refs=None, customer_strategy_refs=None,
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liampauling/betfair | betfairlightweight/endpoints/betting.py | Betting.list_market_profit_and_loss | def list_market_profit_and_loss(self, market_ids, include_settled_bets=None, include_bsp_bets=None,
net_of_commission=None, session=None, lightweight=None):
"""
Retrieve profit and loss for a given list of OPEN markets.
:param list market_ids: List of markets... | python | def list_market_profit_and_loss(self, market_ids, include_settled_bets=None, include_bsp_bets=None,
net_of_commission=None, session=None, lightweight=None):
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Retrieve profit and loss for a given list of OPEN markets.
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liampauling/betfair | betfairlightweight/endpoints/betting.py | Betting.place_orders | def place_orders(self, market_id, instructions, customer_ref=None, market_version=None,
customer_strategy_ref=None, async_=None, session=None, lightweight=None):
"""
Place new orders into market.
:param str market_id: The market id these orders are to be placed on
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Place new orders into market.
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liampauling/betfair | betfairlightweight/streaming/cache.py | MarketBookCache.serialise | def serialise(self):
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'marketId': self.market_id,
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liampauling/betfair | betfairlightweight/endpoints/scores.py | Scores.list_race_details | def list_race_details(self, meeting_ids=None, race_ids=None, session=None, lightweight=None):
"""
Search for races to get their details.
:param dict meeting_ids: Optionally restricts the results to the specified meeting IDs.
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Search for races to get their details.
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liampauling/betfair | betfairlightweight/endpoints/scores.py | Scores.list_available_events | def list_available_events(self, event_ids=None, event_type_ids=None, event_status=None, session=None,
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Search for events that have live score data available.
:param list event_ids: Optionally restricts the results to the specified event IDs
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Search for events that have live score data available.
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liampauling/betfair | betfairlightweight/endpoints/scores.py | Scores.list_scores | def list_scores(self, update_keys, session=None, lightweight=None):
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liampauling/betfair | betfairlightweight/endpoints/scores.py | Scores.list_incidents | def list_incidents(self, update_keys, session=None, lightweight=None):
"""
Returns a list of incidents for the given events.
:param dict update_keys: The filter to select desired markets. All markets that match
the criteria in the filter are selected e.g. [{'eventId': '28205674', 'lastU... | python | def list_incidents(self, update_keys, session=None, lightweight=None):
"""
Returns a list of incidents for the given events.
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liampauling/betfair | betfairlightweight/endpoints/inplayservice.py | InPlayService.get_event_timeline | def get_event_timeline(self, event_id, session=None, lightweight=None):
"""
Returns event timeline for event id provided.
:param int event_id: Event id to return
:param requests.session session: Requests session object
:param bool lightweight: If True will return dict not a reso... | python | def get_event_timeline(self, event_id, session=None, lightweight=None):
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Returns event timeline for event id provided.
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liampauling/betfair | betfairlightweight/endpoints/inplayservice.py | InPlayService.get_event_timelines | def get_event_timelines(self, event_ids, session=None, lightweight=None):
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liampauling/betfair | betfairlightweight/endpoints/inplayservice.py | InPlayService.get_scores | def get_scores(self, event_ids, session=None, lightweight=None):
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liampauling/betfair | betfairlightweight/endpoints/streaming.py | Streaming.create_stream | def create_stream(self, unique_id=0, listener=None, timeout=11, buffer_size=1024, description='BetfairSocket',
host=None):
"""
Creates BetfairStream.
:param dict unique_id: Id used to start unique id's of the stream (+1 before every request)
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liampauling/betfair | betfairlightweight/endpoints/historic.py | Historic.get_my_data | def get_my_data(self, session=None):
"""
Returns a list of data descriptions for data which has been purchased by the signed in user.
:param requests.session session: Requests session object
:rtype: dict
"""
params = clean_locals(locals())
method = 'GetMyData'
... | python | def get_my_data(self, session=None):
"""
Returns a list of data descriptions for data which has been purchased by the signed in user.
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:rtype: dict
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liampauling/betfair | betfairlightweight/endpoints/historic.py | Historic.get_data_size | def get_data_size(self, sport, plan, from_day, from_month, from_year, to_day, to_month, to_year, event_id=None,
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liampauling/betfair | betfairlightweight/endpoints/racecard.py | RaceCard.login | def login(self, session=None):
"""
Parses app key from betfair exchange site.
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session = session or self.client.session
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response = session.get(self.login_url)
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... | python | def login(self, session=None):
"""
Parses app key from betfair exchange site.
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session = session or self.client.session
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response = session.get(self.login_url)
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liampauling/betfair | betfairlightweight/endpoints/racecard.py | RaceCard.get_race_card | def get_race_card(self, market_ids, data_entries=None, session=None, lightweight=None):
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liampauling/betfair | betfairlightweight/streaming/listener.py | StreamListener.on_data | def on_data(self, raw_data):
"""Called when raw data is received from connection.
Override this method if you wish to manually handle
the stream data
:param raw_data: Received raw data
:return: Return False to stop stream and close connection
"""
try:
... | python | def on_data(self, raw_data):
"""Called when raw data is received from connection.
Override this method if you wish to manually handle
the stream data
:param raw_data: Received raw data
:return: Return False to stop stream and close connection
"""
try:
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liampauling/betfair | betfairlightweight/streaming/listener.py | StreamListener._on_connection | def _on_connection(self, data, unique_id):
"""Called on collection operation
:param data: Received data
"""
if unique_id is None:
unique_id = self.stream_unique_id
self.connection_id = data.get('connectionId')
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"""Called on collection operation
:param data: Received data
"""
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unique_id = self.stream_unique_id
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liampauling/betfair | betfairlightweight/streaming/listener.py | StreamListener._on_status | def _on_status(data, unique_id):
"""Called on status operation
:param data: Received data
"""
status_code = data.get('statusCode')
logger.info('[Subscription: %s]: %s' % (unique_id, status_code)) | python | def _on_status(data, unique_id):
"""Called on status operation
:param data: Received data
"""
status_code = data.get('statusCode')
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liampauling/betfair | betfairlightweight/streaming/listener.py | StreamListener._error_handler | def _error_handler(data, unique_id):
"""Called when data first received
:param data: Received data
:param unique_id: Unique id
:return: True if error present
"""
if data.get('statusCode') == 'FAILURE':
logger.error('[Subscription: %s] %s: %s' % (unique_id, da... | python | def _error_handler(data, unique_id):
"""Called when data first received
:param data: Received data
:param unique_id: Unique id
:return: True if error present
"""
if data.get('statusCode') == 'FAILURE':
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liampauling/betfair | betfairlightweight/streaming/betfairstream.py | BetfairStream.stop | def stop(self):
"""Stops read loop and closes socket if it has been created.
"""
self._running = False
if self._socket is None:
return
try:
self._socket.shutdown(socket.SHUT_RDWR)
self._socket.close()
except socket.error:
p... | python | def stop(self):
"""Stops read loop and closes socket if it has been created.
"""
self._running = False
if self._socket is None:
return
try:
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self._socket.close()
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liampauling/betfair | betfairlightweight/streaming/betfairstream.py | BetfairStream.authenticate | def authenticate(self):
"""Authentication request.
"""
unique_id = self.new_unique_id()
message = {
'op': 'authentication',
'id': unique_id,
'appKey': self.app_key,
'session': self.session_token,
}
self._send(message)
... | python | def authenticate(self):
"""Authentication request.
"""
unique_id = self.new_unique_id()
message = {
'op': 'authentication',
'id': unique_id,
'appKey': self.app_key,
'session': self.session_token,
}
self._send(message)
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liampauling/betfair | betfairlightweight/streaming/betfairstream.py | BetfairStream.heartbeat | def heartbeat(self):
"""Heartbeat request to keep session alive.
"""
unique_id = self.new_unique_id()
message = {
'op': 'heartbeat',
'id': unique_id,
}
self._send(message)
return unique_id | python | def heartbeat(self):
"""Heartbeat request to keep session alive.
"""
unique_id = self.new_unique_id()
message = {
'op': 'heartbeat',
'id': unique_id,
}
self._send(message)
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liampauling/betfair | betfairlightweight/streaming/betfairstream.py | BetfairStream.subscribe_to_markets | def subscribe_to_markets(self, market_filter, market_data_filter, initial_clk=None, clk=None,
conflate_ms=None, heartbeat_ms=None, segmentation_enabled=True):
"""
Market subscription request.
:param dict market_filter: Market filter
:param dict market_data_f... | python | def subscribe_to_markets(self, market_filter, market_data_filter, initial_clk=None, clk=None,
conflate_ms=None, heartbeat_ms=None, segmentation_enabled=True):
"""
Market subscription request.
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liampauling/betfair | betfairlightweight/streaming/betfairstream.py | BetfairStream.subscribe_to_orders | def subscribe_to_orders(self, order_filter=None, initial_clk=None, clk=None, conflate_ms=None,
heartbeat_ms=None, segmentation_enabled=True):
"""
Order subscription request.
:param dict order_filter: Order filter to be applied
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"""
Order subscription request.
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liampauling/betfair | betfairlightweight/streaming/betfairstream.py | BetfairStream._create_socket | def _create_socket(self):
"""Creates ssl socket, connects to stream api and
sets timeout.
"""
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s = ssl.wrap_socket(s)
s.connect((self.host, self.__port))
s.settimeout(self.timeout)
return s | python | def _create_socket(self):
"""Creates ssl socket, connects to stream api and
sets timeout.
"""
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s = ssl.wrap_socket(s)
s.connect((self.host, self.__port))
s.settimeout(self.timeout)
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liampauling/betfair | betfairlightweight/streaming/betfairstream.py | BetfairStream._read_loop | def _read_loop(self):
"""Read loop, splits by CRLF and pushes received data
to _data.
"""
while self._running:
received_data_raw = self._receive_all()
if self._running:
self.receive_count += 1
self.datetime_last_received = datetime.... | python | def _read_loop(self):
"""Read loop, splits by CRLF and pushes received data
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"""
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if self._running:
self.receive_count += 1
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liampauling/betfair | betfairlightweight/streaming/betfairstream.py | BetfairStream._receive_all | def _receive_all(self):
"""Whilst socket is running receives data from socket,
till CRLF is detected.
"""
(data, part) = ('', '')
if is_py3:
crlf_bytes = bytes(self.__CRLF, encoding=self.__encoding)
else:
crlf_bytes = self.__CRLF
while sel... | python | def _receive_all(self):
"""Whilst socket is running receives data from socket,
till CRLF is detected.
"""
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crlf_bytes = bytes(self.__CRLF, encoding=self.__encoding)
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liampauling/betfair | betfairlightweight/streaming/betfairstream.py | BetfairStream._data | def _data(self, received_data):
"""Sends data to listener, if False is returned; socket
is closed.
:param received_data: Decoded data received from socket.
"""
if self.listener.on_data(received_data) is False:
self.stop()
raise ListenerError(self.listener... | python | def _data(self, received_data):
"""Sends data to listener, if False is returned; socket
is closed.
:param received_data: Decoded data received from socket.
"""
if self.listener.on_data(received_data) is False:
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liampauling/betfair | betfairlightweight/streaming/betfairstream.py | BetfairStream._send | def _send(self, message):
"""If not running connects socket and
authenticates. Adds CRLF and sends message
to Betfair.
:param message: Data to be sent to Betfair.
"""
if not self._running:
self._connect()
self.authenticate()
message_dumped... | python | def _send(self, message):
"""If not running connects socket and
authenticates. Adds CRLF and sends message
to Betfair.
:param message: Data to be sent to Betfair.
"""
if not self._running:
self._connect()
self.authenticate()
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dmbee/seglearn | seglearn/pipe.py | Pype.fit_transform | def fit_transform(self, X, y=None, **fit_params):
"""
Fit the model and transform with the final estimator
Fits all the transforms one after the other and transforms the
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Parameters
------... | python | def fit_transform(self, X, y=None, **fit_params):
"""
Fit the model and transform with the final estimator
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dmbee/seglearn | seglearn/pipe.py | Pype.predict | def predict(self, X):
"""
Apply transforms to the data, and predict with the final estimator
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
of the pipeline.
Returns
-------
yp ... | python | def predict(self, X):
"""
Apply transforms to the data, and predict with the final estimator
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
of the pipeline.
Returns
-------
yp ... | [
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dmbee/seglearn | seglearn/pipe.py | Pype.transform_predict | def transform_predict(self, X, y):
"""
Apply transforms to the data, and predict with the final estimator.
Unlike predict, this also returns the transformed target
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first... | python | def transform_predict(self, X, y):
"""
Apply transforms to the data, and predict with the final estimator.
Unlike predict, this also returns the transformed target
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X : iterable
Data to predict on. Must fulfill input requirements of first... | [
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dmbee/seglearn | seglearn/pipe.py | Pype.score | def score(self, X, y=None, sample_weight=None):
"""
Apply transforms, and score with the final estimator
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
of the pipeline.
y : iterable, default=No... | python | def score(self, X, y=None, sample_weight=None):
"""
Apply transforms, and score with the final estimator
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
of the pipeline.
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dmbee/seglearn | seglearn/pipe.py | Pype.predict_proba | def predict_proba(self, X):
"""
Apply transforms, and predict_proba of the final estimator
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
of the pipeline.
Returns
-------
y_pro... | python | def predict_proba(self, X):
"""
Apply transforms, and predict_proba of the final estimator
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
of the pipeline.
Returns
-------
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X : iterable
Data to predict on. Must fulfill input requirements of first step
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Returns
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y_proba : array-like, shape = [n_samples, n_classes]
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dmbee/seglearn | seglearn/pipe.py | Pype.decision_function | def decision_function(self, X):
"""
Apply transforms, and decision_function of the final estimator
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
of the pipeline.
Returns
-------
... | python | def decision_function(self, X):
"""
Apply transforms, and decision_function of the final estimator
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
of the pipeline.
Returns
-------
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X : iterable
Data to predict on. Must fulfill input requirements of first step
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Returns
-------
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