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251,400 | MillionIntegrals/vel | vel/metrics/accuracy.py | Accuracy._value_function | def _value_function(self, x_input, y_true, y_pred):
""" Return classification accuracy of input """
if len(y_true.shape) == 1:
return y_pred.argmax(1).eq(y_true).double().mean().item()
else:
raise NotImplementedError | python | def _value_function(self, x_input, y_true, y_pred):
if len(y_true.shape) == 1:
return y_pred.argmax(1).eq(y_true).double().mean().item()
else:
raise NotImplementedError | [
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251,401 | MillionIntegrals/vel | vel/storage/streaming/visdom.py | VisdomStreaming.on_epoch_end | def on_epoch_end(self, epoch_info):
""" Update data in visdom on push """
metrics_df = pd.DataFrame([epoch_info.result]).set_index('epoch_idx')
visdom_append_metrics(
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) | python | def on_epoch_end(self, epoch_info):
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visdom_append_metrics(
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251,402 | MillionIntegrals/vel | vel/storage/streaming/visdom.py | VisdomStreaming.on_batch_end | def on_batch_end(self, batch_info):
""" Stream LR to visdom """
if self.settings.stream_lr:
iteration_idx = (
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lr = bat... | python | def on_batch_end(self, batch_info):
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251,403 | MillionIntegrals/vel | vel/launcher.py | main | def main():
""" Paperboy entry point - parse the arguments and run a command """
parser = argparse.ArgumentParser(description='Paperboy deep learning launcher')
parser.add_argument('config', metavar='FILENAME', help='Configuration file for the run')
parser.add_argument('command', metavar='COMMAND', hel... | python | def main():
parser = argparse.ArgumentParser(description='Paperboy deep learning launcher')
parser.add_argument('config', metavar='FILENAME', help='Configuration file for the run')
parser.add_argument('command', metavar='COMMAND', help='A command to run')
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251,404 | MillionIntegrals/vel | vel/util/random.py | set_seed | def set_seed(seed: int):
""" Set random seed for python, numpy and pytorch RNGs """
random.seed(seed)
np.random.seed(seed)
torch.random.manual_seed(seed) | python | def set_seed(seed: int):
random.seed(seed)
np.random.seed(seed)
torch.random.manual_seed(seed) | [
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251,405 | MillionIntegrals/vel | vel/util/better.py | better | def better(old_value, new_value, mode):
""" Check if new value is better than the old value"""
if (old_value is None or np.isnan(old_value)) and (new_value is not None and not np.isnan(new_value)):
return True
if mode == 'min':
return new_value < old_value
elif mode == 'max':
re... | python | def better(old_value, new_value, mode):
if (old_value is None or np.isnan(old_value)) and (new_value is not None and not np.isnan(new_value)):
return True
if mode == 'min':
return new_value < old_value
elif mode == 'max':
return new_value > old_value
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251,406 | MillionIntegrals/vel | vel/rl/modules/deterministic_critic_head.py | DeterministicCriticHead.reset_weights | def reset_weights(self):
""" Initialize weights to sane defaults """
init.uniform_(self.linear.weight, -3e-3, 3e-3)
init.zeros_(self.linear.bias) | python | def reset_weights(self):
init.uniform_(self.linear.weight, -3e-3, 3e-3)
init.zeros_(self.linear.bias) | [
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251,407 | MillionIntegrals/vel | vel/rl/discount_bootstrap.py | discount_bootstrap | def discount_bootstrap(rewards_buffer, dones_buffer, final_values, discount_factor, number_of_steps):
""" Calculate state values bootstrapping off the following state values """
true_value_buffer = torch.zeros_like(rewards_buffer)
# discount/bootstrap off value fn
current_value = final_values
for ... | python | def discount_bootstrap(rewards_buffer, dones_buffer, final_values, discount_factor, number_of_steps):
true_value_buffer = torch.zeros_like(rewards_buffer)
# discount/bootstrap off value fn
current_value = final_values
for i in reversed(range(number_of_steps)):
current_value = rewards_buffer[i]... | [
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251,408 | MillionIntegrals/vel | vel/internals/model_config.py | ModelConfig.find_project_directory | def find_project_directory(start_path) -> str:
""" Locate top-level project directory """
start_path = os.path.realpath(start_path)
possible_name = os.path.join(start_path, ModelConfig.PROJECT_FILE_NAME)
if os.path.exists(possible_name):
return start_path
else:
... | python | def find_project_directory(start_path) -> str:
start_path = os.path.realpath(start_path)
possible_name = os.path.join(start_path, ModelConfig.PROJECT_FILE_NAME)
if os.path.exists(possible_name):
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up_path = os.path.realpath(os.path.join(star... | [
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251,409 | 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,
device: str = 'cuda', params=None):
""" Create model config from file """
with open(filename, 'r') as fp:
model_config_contents = Parser.parse(fp)
project_config_p... | python | def from_file(cls, filename: str, run_number: int, continue_training: bool = False, seed: int = None,
device: str = 'cuda', params=None):
with open(filename, 'r') as fp:
model_config_contents = Parser.parse(fp)
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251,410 | MillionIntegrals/vel | vel/internals/model_config.py | ModelConfig.from_memory | def from_memory(cls, model_data: dict, run_number: int, project_dir: str,
continue_training=False, seed: int = None, device: str = 'cuda', params=None):
""" Create model config from supplied data """
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251,411 | MillionIntegrals/vel | vel/internals/model_config.py | ModelConfig.run_command | def run_command(self, command_name, varargs):
""" Instantiate model class """
command_descriptor = self.get_command(command_name)
return command_descriptor.run(*varargs) | python | def run_command(self, command_name, varargs):
command_descriptor = self.get_command(command_name)
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251,412 | MillionIntegrals/vel | vel/internals/model_config.py | ModelConfig.project_data_dir | def project_data_dir(self, *args) -> str:
""" Directory where to store data """
return os.path.normpath(os.path.join(self.project_dir, 'data', *args)) | python | def project_data_dir(self, *args) -> str:
return os.path.normpath(os.path.join(self.project_dir, 'data', *args)) | [
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251,413 | MillionIntegrals/vel | vel/internals/model_config.py | ModelConfig.output_dir | def output_dir(self, *args) -> str:
""" Directory where to store output """
return os.path.join(self.project_dir, 'output', *args) | python | def output_dir(self, *args) -> str:
return os.path.join(self.project_dir, 'output', *args) | [
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251,414 | MillionIntegrals/vel | vel/internals/model_config.py | ModelConfig.project_top_dir | def project_top_dir(self, *args) -> str:
""" Project top-level directory """
return os.path.join(self.project_dir, *args) | python | def project_top_dir(self, *args) -> str:
return os.path.join(self.project_dir, *args) | [
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251,415 | MillionIntegrals/vel | vel/internals/model_config.py | ModelConfig.provide_with_default | def provide_with_default(self, name, default=None):
""" Return a dependency-injected instance """
return self.provider.instantiate_by_name_with_default(name, default_value=default) | python | def provide_with_default(self, name, default=None):
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251,416 | douban/libmc | misc/runbench.py | benchmark_method | def benchmark_method(f):
"decorator to turn f into a factory of benchmarks"
@wraps(f)
def inner(name, *args, **kwargs):
return Benchmark(name, f, args, kwargs)
return inner | python | def benchmark_method(f):
"decorator to turn f into a factory of benchmarks"
@wraps(f)
def inner(name, *args, **kwargs):
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251,417 | douban/libmc | misc/runbench.py | bench | def bench(participants=participants, benchmarks=benchmarks,
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"""Do you even lift?"""
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means = [[] for p in participants]
stddevs = [[] for p in participants]
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last_fn = None
fo... | python | def bench(participants=participants, benchmarks=benchmarks,
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mcs = [p.factory() for p in participants]
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251,418 | liampauling/betfair | betfairlightweight/resources/baseresource.py | BaseResource.strip_datetime | def strip_datetime(value):
"""
Converts value to datetime if string or int.
"""
if isinstance(value, basestring):
try:
return parse_datetime(value)
except ValueError:
return
elif isinstance(value, integer_types):
... | python | def strip_datetime(value):
if isinstance(value, basestring):
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except ValueError:
return
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251,419 | liampauling/betfair | betfairlightweight/baseclient.py | BaseClient.set_session_token | def set_session_token(self, session_token):
"""
Sets session token and new login time.
:param str session_token: Session token from request.
"""
self.session_token = session_token
self._login_time = datetime.datetime.now() | python | def set_session_token(self, session_token):
self.session_token = session_token
self._login_time = datetime.datetime.now() | [
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251,420 | liampauling/betfair | betfairlightweight/baseclient.py | BaseClient.get_password | def get_password(self):
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if os.environ.get(self.username+'password'):
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if self.password is None:
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raise PasswordError(self.username) | [
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251,421 | liampauling/betfair | betfairlightweight/baseclient.py | BaseClient.get_app_key | def get_app_key(self):
"""
If app_key is not provided will look in environment
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"""
if self.app_key is None:
if os.environ.get(self.username):
self.app_key = os.environ.get(self.username)
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raise A... | python | def get_app_key(self):
if self.app_key is None:
if os.environ.get(self.username):
self.app_key = os.environ.get(self.username)
else:
raise AppKeyError(self.username) | [
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251,422 | liampauling/betfair | betfairlightweight/baseclient.py | BaseClient.session_expired | def session_expired(self):
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"""
if not self._login_time or (datetime.datetime.now()-self._login_time).total_seconds() > 12000:
return True | python | def session_expired(self):
if not self._login_time or (datetime.datetime.now()-self._login_time).total_seconds() > 12000:
return True | [
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251,423 | liampauling/betfair | betfairlightweight/utils.py | check_status_code | def check_status_code(response, codes=None):
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codes = codes or [200]
if response.status_code not in codes:
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251,424 | 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,
customer_strategy_refs=None, currency_code=None, matched_since=... | python | 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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251,425 | 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,
from_record=None, record_count=None, session=None, lightweight=None):
... | python | def list_current_orders(self, bet_ids=None, market_ids=None, order_projection=None, customer_order_refs=None,
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251,426 | 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,
side=None, settled_date_range=time_range(), group_by=None, include_item_... | python | 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,
side=None, settled_date_range=time_range(), group_by=None, include_item_... | [
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251,427 | 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):
params = clean_locals(locals())
method = '%s%s' % (self.URI, 'listMarketProfitAndLoss')
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251,428 | 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
:... | python | def place_orders(self, market_id, instructions, customer_ref=None, market_version=None,
customer_strategy_ref=None, async_=None, session=None, lightweight=None):
params = clean_locals(locals())
method = '%s%s' % (self.URI, 'placeOrders')
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251,429 | liampauling/betfair | betfairlightweight/streaming/cache.py | MarketBookCache.serialise | def serialise(self):
"""Creates standard market book json response,
will error if EX_MARKET_DEF not incl.
"""
return {
'marketId': self.market_id,
'totalAvailable': None,
'isMarketDataDelayed': None,
'lastMatchTime': None,
'betD... | python | def serialise(self):
return {
'marketId': self.market_id,
'totalAvailable': None,
'isMarketDataDelayed': None,
'lastMatchTime': None,
'betDelay': self.market_definition.get('betDelay'),
'version': self.market_definition.get('version'),
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251,430 | 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.
The unique Id for the meeting equivalent to the eventId for th... | python | def list_race_details(self, meeting_ids=None, race_ids=None, session=None, lightweight=None):
params = clean_locals(locals())
method = '%s%s' % (self.URI, 'listRaceDetails')
(response, elapsed_time) = self.request(method, params, session)
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251,431 | 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,
lightweight=None):
"""
Search for events that have live score data available.
:param list event_ids: Optionally restricts the results to the specified event IDs
... | python | def list_available_events(self, event_ids=None, event_type_ids=None, event_status=None, session=None,
lightweight=None):
params = clean_locals(locals())
method = '%s%s' % (self.URI, 'listAvailableEvents')
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251,432 | liampauling/betfair | betfairlightweight/endpoints/scores.py | Scores.list_scores | def list_scores(self, update_keys, session=None, lightweight=None):
"""
Returns a list of current scores for the given events.
:param list update_keys: The filter to select desired markets. All markets that match
the criteria in the filter are selected e.g. [{'eventId': '28205674', 'las... | python | def list_scores(self, update_keys, session=None, lightweight=None):
params = clean_locals(locals())
method = '%s%s' % (self.URI, 'listScores')
(response, elapsed_time) = self.request(method, params, session)
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251,433 | 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):
params = clean_locals(locals())
method = '%s%s' % (self.URI, 'listIncidents')
(response, elapsed_time) = self.request(method, params, session)
return self.process_response(response, resources.Incidents, elapsed_time, ... | [
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251,434 | 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):
url = '%s%s' % (self.url, 'eventTimeline')
params = {
'eventId': event_id,
'alt': 'json',
'regionCode': 'UK',
'locale': 'en_GB'
}
(response, elapsed_time) = self.reques... | [
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251,435 | liampauling/betfair | betfairlightweight/endpoints/inplayservice.py | InPlayService.get_event_timelines | def get_event_timelines(self, event_ids, session=None, lightweight=None):
"""
Returns a list of event timelines based on event id's
supplied.
:param list event_ids: List of event id's to return
:param requests.session session: Requests session object
:param bool lightwei... | python | def get_event_timelines(self, event_ids, session=None, lightweight=None):
url = '%s%s' % (self.url, 'eventTimelines')
params = {
'eventIds': ','.join(str(x) for x in event_ids),
'alt': 'json',
'regionCode': 'UK',
'locale': 'en_GB'
}
(respon... | [
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251,436 | liampauling/betfair | betfairlightweight/endpoints/inplayservice.py | InPlayService.get_scores | def get_scores(self, event_ids, session=None, lightweight=None):
"""
Returns a list of scores based on event id's
supplied.
:param list event_ids: List of event id's to return
:param requests.session session: Requests session object
:param bool lightweight: If True will ... | python | def get_scores(self, event_ids, session=None, lightweight=None):
url = '%s%s' % (self.url, 'scores')
params = {
'eventIds': ','.join(str(x) for x in event_ids),
'alt': 'json',
'regionCode': 'UK',
'locale': 'en_GB'
}
(response, elapsed_time)... | [
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251,437 | 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)
:param resources.Listen... | python | def create_stream(self, unique_id=0, listener=None, timeout=11, buffer_size=1024, description='BetfairSocket',
host=None):
listener = listener if listener else BaseListener()
return BetfairStream(
unique_id,
listener,
app_key=self.client.app_key,... | [
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251,438 | 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):
params = clean_locals(locals())
method = 'GetMyData'
(response, elapsed_time) = self.request(method, params, session)
return response | [
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251,439 | 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,
event_name=None, market_types_collection=None, countries_collection=None,
file_type_collection=None, session=None):
"""
Returns a dictionary of file... | python | def get_data_size(self, sport, plan, from_day, from_month, from_year, to_day, to_month, to_year, event_id=None,
event_name=None, market_types_collection=None, countries_collection=None,
file_type_collection=None, session=None):
params = clean_locals(locals())
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251,440 | liampauling/betfair | betfairlightweight/endpoints/racecard.py | RaceCard.login | def login(self, session=None):
"""
Parses app key from betfair exchange site.
:param requests.session session: Requests session object
"""
session = session or self.client.session
try:
response = session.get(self.login_url)
except ConnectionError:
... | python | def login(self, session=None):
session = session or self.client.session
try:
response = session.get(self.login_url)
except ConnectionError:
raise APIError(None, self.login_url, None, 'ConnectionError')
except Exception as e:
raise APIError(None, self.l... | [
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251,441 | liampauling/betfair | betfairlightweight/endpoints/racecard.py | RaceCard.get_race_card | def get_race_card(self, market_ids, data_entries=None, session=None, lightweight=None):
"""
Returns a list of race cards based on market ids provided.
:param list market_ids: The filter to select desired markets
:param str data_entries: Data to be returned
:param requests.sessio... | python | def get_race_card(self, market_ids, data_entries=None, session=None, lightweight=None):
if not self.app_key:
raise RaceCardError("You need to login before requesting a race_card\n"
"APIClient.race_card.login()")
params = self.create_race_card_req(market_ids, d... | [
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251,442 | 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):
try:
data = json.loads(raw_data)
except ValueError:
logger.error('value error: %s' % raw_data)
return
unique_id = data.get('id')
if self._error_handler(data, unique_id):
return False
operation = data[... | [
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251,443 | 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')
logger.info('[Connect: %s]: connection_id: %s' % ... | python | def _on_connection(self, data, unique_id):
if unique_id is None:
unique_id = self.stream_unique_id
self.connection_id = data.get('connectionId')
logger.info('[Connect: %s]: connection_id: %s' % (unique_id, self.connection_id)) | [
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251,444 | 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):
status_code = data.get('statusCode')
logger.info('[Subscription: %s]: %s' % (unique_id, status_code)) | [
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251,445 | 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):
if data.get('statusCode') == 'FAILURE':
logger.error('[Subscription: %s] %s: %s' % (unique_id, data.get('errorCode'), data.get('errorMessage')))
if data.get('connectionClosed'):
return True
if data.get('status'):
# ... | [
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251,446 | 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):
self._running = False
if self._socket is None:
return
try:
self._socket.shutdown(socket.SHUT_RDWR)
self._socket.close()
except socket.error:
pass
self._socket = None | [
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251,447 | 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):
unique_id = self.new_unique_id()
message = {
'op': 'authentication',
'id': unique_id,
'appKey': self.app_key,
'session': self.session_token,
}
self._send(message)
return unique_id | [
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251,448 | 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):
unique_id = self.new_unique_id()
message = {
'op': 'heartbeat',
'id': unique_id,
}
self._send(message)
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251,449 | 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):
unique_id = self.new_unique_id()
message = {
'op': 'marketSubscription',
'id': unique_id,
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251,450 | 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
:param str initial_clk: Sequence... | python | def subscribe_to_orders(self, order_filter=None, initial_clk=None, clk=None, conflate_ms=None,
heartbeat_ms=None, segmentation_enabled=True):
unique_id = self.new_unique_id()
message = {
'op': 'orderSubscription',
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251,451 | 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):
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 | [
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251,452 | 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):
while self._running:
received_data_raw = self._receive_all()
if self._running:
self.receive_count += 1
self.datetime_last_received = datetime.datetime.utcnow()
received_data_split = received_data_raw.split(self.__CRLF)... | [
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251,453 | 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
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(data, part) = ('', '')
if is_py3:
crlf_bytes = bytes(self.__CRLF, encoding=self.__encoding)
else:
crlf_bytes = self.__CRLF
while self._running and part[-2:] != crlf_bytes:
try:
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251,454 | 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):
if self.listener.on_data(received_data) is False:
self.stop()
raise ListenerError(self.listener.connection_id, received_data) | [
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251,455 | 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 self._running:
self._connect()
self.authenticate()
message_dumped = json.dumps(message) + self.__CRLF
try:
self._socket.send(message_dumped.encode())
except (socket.timeout, socket.error) as e:
self.stop()
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251,456 | 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
data, then uses fit_transform on transformed data with the final
estimator.
Parameters
------... | python | def fit_transform(self, X, y=None, **fit_params):
Xt, yt, fit_params = self._fit(X, y, **fit_params)
if isinstance(self._final_estimator, XyTransformerMixin):
Xt, yt, _ = self._final_estimator.fit_transform(Xt, yt)
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if hasattr(self._final_estimator, 'fit_transform')... | [
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251,457 | 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):
Xt, _, _ = self._transform(X)
return self._final_estimator.predict(Xt) | [
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251,458 | 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):
Xt, yt, _ = self._transform(X, y)
yp = self._final_estimator.predict(Xt)
return yt, yp | [
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251,459 | 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):
Xt, yt, swt = self._transform(X, y, sample_weight)
self.N_test = len(yt)
score_params = {}
if swt is not None:
score_params['sample_weight'] = swt
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251,460 | 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
-------
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Xt, _, _ = self._transform(X)
return self._final_estimator.predict_proba(Xt) | [
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251,461 | 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):
Xt, _, _ = self._transform(X)
return self._final_estimator.decision_function(Xt) | [
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251,462 | dmbee/seglearn | seglearn/pipe.py | Pype.predict_log_proba | def predict_log_proba(self, X):
"""
Apply transforms, and predict_log_proba of the final estimator
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
of the pipeline.
Returns
-------
... | python | def predict_log_proba(self, X):
Xt, _, _ = self._transform(X)
return self._final_estimator.predict_log_proba(Xt) | [
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251,463 | dmbee/seglearn | seglearn/feature_functions.py | base_features | def base_features():
''' Returns dictionary of some basic features that can be calculated for segmented time
series data '''
features = {'mean': mean,
'median': median,
'abs_energy': abs_energy,
'std': std,
'var': var,
'min': mi... | python | def base_features():
''' Returns dictionary of some basic features that can be calculated for segmented time
series data '''
features = {'mean': mean,
'median': median,
'abs_energy': abs_energy,
'std': std,
'var': var,
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251,464 | dmbee/seglearn | seglearn/feature_functions.py | all_features | def all_features():
''' Returns dictionary of all features in the module
.. note:: Some of the features (hist4, corr) are relatively expensive to compute
'''
features = {'mean': mean,
'median': median,
'gmean': gmean,
'hmean': hmean,
'vec_... | python | def all_features():
''' Returns dictionary of all features in the module
.. note:: Some of the features (hist4, corr) are relatively expensive to compute
'''
features = {'mean': mean,
'median': median,
'gmean': gmean,
'hmean': hmean,
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251,465 | dmbee/seglearn | seglearn/feature_functions.py | emg_features | def emg_features(threshold=0):
'''Return a dictionary of popular features used for EMG time series classification.'''
return {
'mean_abs_value': mean_abs,
'zero_crossings': zero_crossing(threshold),
'slope_sign_changes': slope_sign_changes(threshold),
'waveform_length': waveform_... | python | def emg_features(threshold=0):
'''Return a dictionary of popular features used for EMG time series classification.'''
return {
'mean_abs_value': mean_abs,
'zero_crossings': zero_crossing(threshold),
'slope_sign_changes': slope_sign_changes(threshold),
'waveform_length': waveform_... | [
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251,466 | dmbee/seglearn | seglearn/feature_functions.py | means_abs_diff | def means_abs_diff(X):
''' mean absolute temporal derivative '''
return np.mean(np.abs(np.diff(X, axis=1)), axis=1) | python | def means_abs_diff(X):
''' mean absolute temporal derivative '''
return np.mean(np.abs(np.diff(X, axis=1)), axis=1) | [
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251,467 | dmbee/seglearn | seglearn/feature_functions.py | mse | def mse(X):
''' computes mean spectral energy for each variable in a segmented time series '''
return np.mean(np.square(np.abs(np.fft.fft(X, axis=1))), axis=1) | python | def mse(X):
''' computes mean spectral energy for each variable in a segmented time series '''
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251,468 | dmbee/seglearn | seglearn/feature_functions.py | mean_crossings | def mean_crossings(X):
''' Computes number of mean crossings for each variable in a segmented time series '''
X = np.atleast_3d(X)
N = X.shape[0]
D = X.shape[2]
mnx = np.zeros((N, D))
for i in range(D):
pos = X[:, :, i] > 0
npos = ~pos
c = (pos[:, :-1] & npos[:, 1:]) | (n... | python | def mean_crossings(X):
''' Computes number of mean crossings for each variable in a segmented time series '''
X = np.atleast_3d(X)
N = X.shape[0]
D = X.shape[2]
mnx = np.zeros((N, D))
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pos = X[:, :, i] > 0
npos = ~pos
c = (pos[:, :-1] & npos[:, 1:]) | (n... | [
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251,469 | dmbee/seglearn | seglearn/feature_functions.py | corr2 | def corr2(X):
''' computes correlations between all variable pairs in a segmented time series
.. note:: this feature is expensive to compute with the current implementation, and cannot be
used with univariate time series
'''
X = np.atleast_3d(X)
N = X.shape[0]
D = X.shape[2]
if D == 1:... | python | def corr2(X):
''' computes correlations between all variable pairs in a segmented time series
.. note:: this feature is expensive to compute with the current implementation, and cannot be
used with univariate time series
'''
X = np.atleast_3d(X)
N = X.shape[0]
D = X.shape[2]
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251,470 | dmbee/seglearn | seglearn/feature_functions.py | waveform_length | def waveform_length(X):
''' cumulative length of the waveform over a segment for each variable in the segmented time
series '''
return np.sum(np.abs(np.diff(X, axis=1)), axis=1) | python | def waveform_length(X):
''' cumulative length of the waveform over a segment for each variable in the segmented time
series '''
return np.sum(np.abs(np.diff(X, axis=1)), axis=1) | [
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251,471 | dmbee/seglearn | seglearn/feature_functions.py | root_mean_square | def root_mean_square(X):
''' root mean square for each variable in the segmented time series '''
segment_width = X.shape[1]
return np.sqrt(np.sum(X * X, axis=1) / segment_width) | python | def root_mean_square(X):
''' root mean square for each variable in the segmented time series '''
segment_width = X.shape[1]
return np.sqrt(np.sum(X * X, axis=1) / segment_width) | [
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251,472 | dmbee/seglearn | seglearn/split.py | TemporalKFold.split | def split(self, X, y):
'''
Splits time series data and target arrays, and generates splitting indices
Parameters
----------
X : array-like, shape [n_series, ...]
Time series data and (optionally) contextual data
y : array-like shape [n_series, ]
ta... | python | def split(self, X, y):
'''
Splits time series data and target arrays, and generates splitting indices
Parameters
----------
X : array-like, shape [n_series, ...]
Time series data and (optionally) contextual data
y : array-like shape [n_series, ]
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251,473 | dmbee/seglearn | seglearn/split.py | TemporalKFold._ts_slice | def _ts_slice(self, Xt, y):
''' takes time series data, and splits each series into temporal folds '''
Ns = len(Xt)
Xt_new = []
for i in range(self.n_splits):
for j in range(Ns):
Njs = int(len(Xt[j]) / self.n_splits)
Xt_new.append(Xt[j][(Njs * ... | python | def _ts_slice(self, Xt, y):
''' takes time series data, and splits each series into temporal folds '''
Ns = len(Xt)
Xt_new = []
for i in range(self.n_splits):
for j in range(Ns):
Njs = int(len(Xt[j]) / self.n_splits)
Xt_new.append(Xt[j][(Njs * ... | [
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251,474 | dmbee/seglearn | seglearn/split.py | TemporalKFold._make_indices | def _make_indices(self, Ns):
''' makes indices for cross validation '''
N_new = int(Ns * self.n_splits)
test = [np.full(N_new, False) for i in range(self.n_splits)]
for i in range(self.n_splits):
test[i][np.arange(Ns * i, Ns * (i + 1))] = True
train = [np.logical_not... | python | def _make_indices(self, Ns):
''' makes indices for cross validation '''
N_new = int(Ns * self.n_splits)
test = [np.full(N_new, False) for i in range(self.n_splits)]
for i in range(self.n_splits):
test[i][np.arange(Ns * i, Ns * (i + 1))] = True
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251,475 | dmbee/seglearn | seglearn/preprocessing.py | TargetRunLengthEncoder.transform | def transform(self, X, y, sample_weight=None):
'''
Transforms the time series data with run length encoding of the target variable
Note this transformation changes the number of samples in the data
If sample_weight is provided, it is transformed to align to the new target encoding
... | python | def transform(self, X, y, sample_weight=None):
'''
Transforms the time series data with run length encoding of the target variable
Note this transformation changes the number of samples in the data
If sample_weight is provided, it is transformed to align to the new target encoding
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251,476 | dmbee/seglearn | seglearn/preprocessing.py | TargetRunLengthEncoder._rle | def _rle(self, a):
'''
rle implementation credit to Thomas Browne from his SOF post Sept 2015
Parameters
----------
a : array, shape[n,]
input vector
Returns
-------
z : array, shape[nt,]
run lengths
p : array, shape[nt,]
... | python | def _rle(self, a):
'''
rle implementation credit to Thomas Browne from his SOF post Sept 2015
Parameters
----------
a : array, shape[n,]
input vector
Returns
-------
z : array, shape[nt,]
run lengths
p : array, shape[nt,]
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251,477 | dmbee/seglearn | seglearn/preprocessing.py | TargetRunLengthEncoder._transform | def _transform(self, X, y):
'''
Transforms single series
'''
z, p, y_rle = self._rle(y)
p = np.append(p, len(y))
big_enough = p[1:] - p[:-1] >= self.min_length
Xt = []
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'''
Transforms single series
'''
z, p, y_rle = self._rle(y)
p = np.append(p, len(y))
big_enough = p[1:] - p[:-1] >= self.min_length
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251,478 | dmbee/seglearn | seglearn/util.py | get_ts_data_parts | def get_ts_data_parts(X):
'''
Separates time series data object into time series variables and contextual variables
Parameters
----------
X : array-like, shape [n_series, ...]
Time series data and (optionally) contextual data
Returns
-------
Xt : array-like, shape [n_series, ]
... | python | def get_ts_data_parts(X):
'''
Separates time series data object into time series variables and contextual variables
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X : array-like, shape [n_series, ...]
Time series data and (optionally) contextual data
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Xt : array-like, shape [n_series, ]
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251,479 | dmbee/seglearn | seglearn/util.py | check_ts_data_with_ts_target | def check_ts_data_with_ts_target(X, y=None):
'''
Checks time series data with time series target is good. If not raises value error.
Parameters
----------
X : array-like, shape [n_series, ...]
Time series data and (optionally) contextual data
y : array-like, shape [n_series, ...]
... | python | def check_ts_data_with_ts_target(X, y=None):
'''
Checks time series data with time series target is good. If not raises value error.
Parameters
----------
X : array-like, shape [n_series, ...]
Time series data and (optionally) contextual data
y : array-like, shape [n_series, ...]
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251,480 | dmbee/seglearn | seglearn/util.py | ts_stats | def ts_stats(Xt, y, fs=1.0, class_labels=None):
'''
Generates some helpful statistics about the data X
Parameters
----------
X : array-like, shape [n_series, ...]
Time series data and (optionally) contextual data
y : array-like, shape [n_series]
target data
fs : float
... | python | def ts_stats(Xt, y, fs=1.0, class_labels=None):
'''
Generates some helpful statistics about the data X
Parameters
----------
X : array-like, shape [n_series, ...]
Time series data and (optionally) contextual data
y : array-like, shape [n_series]
target data
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251,481 | dmbee/seglearn | seglearn/datasets.py | load_watch | def load_watch():
'''
Loads some of the 6-axis inertial sensor data from my smartwatch project. The sensor data was
recorded as study subjects performed sets of 20 shoulder exercise repetitions while wearing a
smartwatch. It is a multivariate time series.
The study can be found here: https://arxiv.... | python | def load_watch():
'''
Loads some of the 6-axis inertial sensor data from my smartwatch project. The sensor data was
recorded as study subjects performed sets of 20 shoulder exercise repetitions while wearing a
smartwatch. It is a multivariate time series.
The study can be found here: https://arxiv.... | [
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251,482 | dmbee/seglearn | seglearn/transform.py | shuffle_data | def shuffle_data(X, y=None, sample_weight=None):
''' Shuffles indices X, y, and sample_weight together'''
if len(X) > 1:
ind = np.arange(len(X), dtype=np.int)
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Xt = X[ind]
yt = y
swt = sample_weight
if yt is not None:
yt = yt[ind... | python | def shuffle_data(X, y=None, sample_weight=None):
''' Shuffles indices X, y, and sample_weight together'''
if len(X) > 1:
ind = np.arange(len(X), dtype=np.int)
np.random.shuffle(ind)
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251,483 | dmbee/seglearn | seglearn/transform.py | expand_variables_to_segments | def expand_variables_to_segments(v, Nt):
''' expands contextual variables v, by repeating each instance as specified in Nt '''
N_v = len(np.atleast_1d(v[0]))
return np.concatenate([np.full((Nt[i], N_v), v[i]) for i in np.arange(len(v))]) | python | def expand_variables_to_segments(v, Nt):
''' expands contextual variables v, by repeating each instance as specified in Nt '''
N_v = len(np.atleast_1d(v[0]))
return np.concatenate([np.full((Nt[i], N_v), v[i]) for i in np.arange(len(v))]) | [
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251,484 | dmbee/seglearn | seglearn/transform.py | sliding_window | def sliding_window(time_series, width, step, order='F'):
'''
Segments univariate time series with sliding window
Parameters
----------
time_series : array like shape [n_samples]
time series or sequence
width : int > 0
segment width in samples
step : int > 0
stepsize ... | python | def sliding_window(time_series, width, step, order='F'):
'''
Segments univariate time series with sliding window
Parameters
----------
time_series : array like shape [n_samples]
time series or sequence
width : int > 0
segment width in samples
step : int > 0
stepsize ... | [
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251,485 | dmbee/seglearn | seglearn/transform.py | sliding_tensor | def sliding_tensor(mv_time_series, width, step, order='F'):
'''
segments multivariate time series with sliding window
Parameters
----------
mv_time_series : array like shape [n_samples, n_variables]
multivariate time series or sequence
width : int > 0
segment width in samples
... | python | def sliding_tensor(mv_time_series, width, step, order='F'):
'''
segments multivariate time series with sliding window
Parameters
----------
mv_time_series : array like shape [n_samples, n_variables]
multivariate time series or sequence
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segment width in samples
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251,486 | dmbee/seglearn | seglearn/transform.py | SegmentXY.transform | def transform(self, X, y=None, sample_weight=None):
'''
Transforms the time series data into segments
Note this transformation changes the number of samples in the data
If y is provided, it is segmented and transformed to align to the new samples as per
``y_func``
Current... | python | def transform(self, X, y=None, sample_weight=None):
'''
Transforms the time series data into segments
Note this transformation changes the number of samples in the data
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251,487 | dmbee/seglearn | seglearn/transform.py | PadTrunc.transform | def transform(self, X, y=None, sample_weight=None):
'''
Transforms the time series data into fixed length segments using padding and or truncation
If y is a time series and passed, it will be transformed as well
Parameters
----------
X : array-like, shape [n_series, ...]... | python | def transform(self, X, y=None, sample_weight=None):
'''
Transforms the time series data into fixed length segments using padding and or truncation
If y is a time series and passed, it will be transformed as well
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----------
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251,488 | dmbee/seglearn | seglearn/transform.py | InterpLongToWide._check_data | def _check_data(self, X):
'''
Checks that unique identifiers vaf_types are consistent between time series.
Parameters
----------
X : array-like, shape [n_series, ...]
Time series data and (optionally) contextual data
'''
if len(X) > 1:
sv... | python | def _check_data(self, X):
'''
Checks that unique identifiers vaf_types are consistent between time series.
Parameters
----------
X : array-like, shape [n_series, ...]
Time series data and (optionally) contextual data
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251,489 | dmbee/seglearn | seglearn/transform.py | FeatureRep._check_features | def _check_features(self, features, Xti):
'''
tests output of each feature against a segmented time series X
Parameters
----------
features : dict
feature function dictionary
Xti : array-like, shape [n_samples, segment_width, n_variables]
segmente... | python | def _check_features(self, features, Xti):
'''
tests output of each feature against a segmented time series X
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----------
features : dict
feature function dictionary
Xti : array-like, shape [n_samples, segment_width, n_variables]
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251,490 | dmbee/seglearn | seglearn/transform.py | FeatureRep._generate_feature_labels | def _generate_feature_labels(self, X):
'''
Generates string feature labels
'''
Xt, Xc = get_ts_data_parts(X)
ftr_sizes = self._check_features(self.features, Xt[0:3])
f_labels = []
# calculated features
for key in ftr_sizes:
for i in range(ftr... | python | def _generate_feature_labels(self, X):
'''
Generates string feature labels
'''
Xt, Xc = get_ts_data_parts(X)
ftr_sizes = self._check_features(self.features, Xt[0:3])
f_labels = []
# calculated features
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] | d8d7039e92c4c6571a70350c03298aceab8dbeec | https://github.com/dmbee/seglearn/blob/d8d7039e92c4c6571a70350c03298aceab8dbeec/seglearn/transform.py#L1167-L1187 |
251,491 | dmbee/seglearn | seglearn/transform.py | FeatureRepMix._retrieve_indices | def _retrieve_indices(cols):
'''
Retrieve a list of indices corresponding to the provided column specification.
'''
if isinstance(cols, int):
return [cols]
elif isinstance(cols, slice):
start = cols.start if cols.start else 0
stop = cols.stop
... | python | def _retrieve_indices(cols):
'''
Retrieve a list of indices corresponding to the provided column specification.
'''
if isinstance(cols, int):
return [cols]
elif isinstance(cols, slice):
start = cols.start if cols.start else 0
stop = cols.stop
... | [
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251,492 | dmbee/seglearn | seglearn/transform.py | FeatureRepMix._validate | def _validate(self):
'''
Internal function to validate the transformer before applying all internal transformers.
'''
if self.f_labels is None:
raise NotFittedError('FeatureRepMix')
if not self.transformers:
return
names, transformers, _ = zip(*s... | python | def _validate(self):
'''
Internal function to validate the transformer before applying all internal transformers.
'''
if self.f_labels is None:
raise NotFittedError('FeatureRepMix')
if not self.transformers:
return
names, transformers, _ = zip(*s... | [
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251,493 | dmbee/seglearn | seglearn/transform.py | FunctionTransformer.transform | def transform(self, X):
'''
Transforms the time series data based on the provided function. Note this transformation
must not change the number of samples in the data.
Parameters
----------
X : array-like, shape [n_samples, ...]
time series data and (optional... | python | def transform(self, X):
'''
Transforms the time series data based on the provided function. Note this transformation
must not change the number of samples in the data.
Parameters
----------
X : array-like, shape [n_samples, ...]
time series data and (optional... | [
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X : array-like, shape [n_samples, ...]
time series data and (optionally) contextual data
Returns
... | [
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251,494 | SAP/PyHDB | pyhdb/protocol/segments.py | RequestSegment.build_payload | def build_payload(self, payload):
"""Build payload of all parts and write them into the payload buffer"""
remaining_size = self.MAX_SEGMENT_PAYLOAD_SIZE
for part in self.parts:
part_payload = part.pack(remaining_size)
payload.write(part_payload)
remaining_siz... | python | def build_payload(self, payload):
remaining_size = self.MAX_SEGMENT_PAYLOAD_SIZE
for part in self.parts:
part_payload = part.pack(remaining_size)
payload.write(part_payload)
remaining_size -= len(part_payload) | [
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251,495 | SAP/PyHDB | pyhdb/protocol/types.py | escape | def escape(value):
"""
Escape a single value.
"""
if isinstance(value, (tuple, list)):
return "(" + ", ".join([escape(arg) for arg in value]) + ")"
else:
typ = by_python_type.get(value.__class__)
if typ is None:
raise InterfaceError(
"Unsupported ... | python | def escape(value):
if isinstance(value, (tuple, list)):
return "(" + ", ".join([escape(arg) for arg in value]) + ")"
else:
typ = by_python_type.get(value.__class__)
if typ is None:
raise InterfaceError(
"Unsupported python input: %s (%s)" % (value, value.__cla... | [
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251,496 | SAP/PyHDB | pyhdb/protocol/types.py | escape_values | def escape_values(values):
"""
Escape multiple values from a list, tuple or dict.
"""
if isinstance(values, (tuple, list)):
return tuple([escape(value) for value in values])
elif isinstance(values, dict):
return dict([
(key, escape(value)) for (key, value) in values.items... | python | def escape_values(values):
if isinstance(values, (tuple, list)):
return tuple([escape(value) for value in values])
elif isinstance(values, dict):
return dict([
(key, escape(value)) for (key, value) in values.items()
])
else:
raise InterfaceError("escape_values exp... | [
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251,497 | SAP/PyHDB | pyhdb/protocol/types.py | Date.prepare | def prepare(cls, value):
"""Pack datetime value into proper binary format"""
pfield = struct.pack('b', cls.type_code)
if isinstance(value, string_types):
value = datetime.datetime.strptime(value, "%Y-%m-%d")
year = value.year | 0x8000 # for some unknown reasons year has to b... | python | def prepare(cls, value):
pfield = struct.pack('b', cls.type_code)
if isinstance(value, string_types):
value = datetime.datetime.strptime(value, "%Y-%m-%d")
year = value.year | 0x8000 # for some unknown reasons year has to be bit-or'ed with 0x8000
month = value.month - 1 ... | [
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251,498 | SAP/PyHDB | pyhdb/protocol/types.py | Time.prepare | def prepare(cls, value):
"""Pack time value into proper binary format"""
pfield = struct.pack('b', cls.type_code)
if isinstance(value, string_types):
if "." in value:
value = datetime.datetime.strptime(value, "%H:%M:%S.%f")
else:
value = da... | python | def prepare(cls, value):
pfield = struct.pack('b', cls.type_code)
if isinstance(value, string_types):
if "." in value:
value = datetime.datetime.strptime(value, "%H:%M:%S.%f")
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value = datetime.datetime.strptime(value, "%H:%M:%S")
mill... | [
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251,499 | SAP/PyHDB | pyhdb/protocol/types.py | MixinLobType.prepare | def prepare(cls, value, length=0, position=0, is_last_data=True):
"""Prepare Lob header.
Note that the actual lob data is NOT written here but appended after the parameter block for each row!
"""
hstruct = WriteLobHeader.header_struct
lob_option_dataincluded = WriteLobHeader.LOB_... | python | def prepare(cls, value, length=0, position=0, is_last_data=True):
hstruct = WriteLobHeader.header_struct
lob_option_dataincluded = WriteLobHeader.LOB_OPTION_DATAINCLUDED if length > 0 else 0
lob_option_lastdata = WriteLobHeader.LOB_OPTION_LASTDATA if is_last_data else 0
options = lob_opt... | [
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