Search is not available for this dataset
identifier stringlengths 1 155 | parameters stringlengths 2 6.09k | docstring stringlengths 11 63.4k | docstring_summary stringlengths 0 63.4k | function stringlengths 29 99.8k | function_tokens list | start_point list | end_point list | language stringclasses 1
value | docstring_language stringlengths 2 7 | docstring_language_predictions stringlengths 18 23 | is_langid_reliable stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|---|---|
FirmataBinaryDigitalInput.latch_callback | (self, data: list) | Update pin state on callback. | Update pin state on callback. | async def latch_callback(self, data: list) -> None:
"""Update pin state on callback."""
if data[1] != self._firmata_pin:
return
_LOGGER.debug(
"Received latch %d for digital input pin %d on board %s",
data[2],
self._firmata_pin,
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FirmataAnalogInput.__init__ | (
self, board: FirmataBoard, pin: FirmataPinType, pin_mode: str, differential: int
) | Initialize the analog input pin. | Initialize the analog input pin. | def __init__(
self, board: FirmataBoard, pin: FirmataPinType, pin_mode: str, differential: int
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"""Initialize the analog input pin."""
self._differential = differential
self._forward_callback = None
super().__init__(board, pin, pin_mode) | [
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FirmataAnalogInput.start_pin | (self, forward_callback: Callable[[], None]) | Get initial state and start reporting a pin. | Get initial state and start reporting a pin. | async def start_pin(self, forward_callback: Callable[[], None]) -> None:
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FirmataAnalogInput.stop_pin | (self) | Stop reporting analog input pin. | Stop reporting analog input pin. | async def stop_pin(self) -> None:
"""Stop reporting analog input pin."""
_LOGGER.debug(
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FirmataAnalogInput.state | (self) | Return sensor state. | Return sensor state. | def state(self) -> int:
"""Return sensor state."""
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FirmataAnalogInput.latch_callback | (self, data: list) | Update pin state on callback. | Update pin state on callback. | async def latch_callback(self, data: list) -> None:
"""Update pin state on callback."""
if data[1] != self._analog_pin:
return
_LOGGER.debug(
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_async_has_devices | (hass) | Return if there are devices that can be discovered. | Return if there are devices that can be discovered. | async def _async_has_devices(hass):
"""Return if there are devices that can be discovered."""
aehw4a1_ip_addresses = await AehW4a1().discovery()
return len(aehw4a1_ip_addresses) > 0 | [
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setup | (hass, config) | Set up the Graphite feeder. | Set up the Graphite feeder. | def setup(hass, config):
"""Set up the Graphite feeder."""
conf = config[DOMAIN]
host = conf.get(CONF_HOST)
prefix = conf.get(CONF_PREFIX)
port = conf.get(CONF_PORT)
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
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GraphiteFeeder.__init__ | (self, hass, host, port, prefix) | Initialize the feeder. | Initialize the feeder. | def __init__(self, hass, host, port, prefix):
"""Initialize the feeder."""
super().__init__(daemon=True)
self._hass = hass
self._host = host
self._port = port
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GraphiteFeeder.start_listen | (self, event) | Start event-processing thread. | Start event-processing thread. | def start_listen(self, event):
"""Start event-processing thread."""
_LOGGER.debug("Event processing thread started")
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GraphiteFeeder.shutdown | (self, event) | Signal shutdown of processing event. | Signal shutdown of processing event. | def shutdown(self, event):
"""Signal shutdown of processing event."""
_LOGGER.debug("Event processing signaled exit")
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GraphiteFeeder.event_listener | (self, event) | Queue an event for processing. | Queue an event for processing. | def event_listener(self, event):
"""Queue an event for processing."""
if self.is_alive() or not self._we_started:
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GraphiteFeeder._send_to_graphite | (self, data) | Send data to Graphite. | Send data to Graphite. | def _send_to_graphite(self, data):
"""Send data to Graphite."""
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
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GraphiteFeeder._report_attributes | (self, entity_id, new_state) | Report the attributes. | Report the attributes. | def _report_attributes(self, entity_id, new_state):
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"%s.%s.%s %... | [
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GraphiteFeeder.run | (self) | Run the process to export the data. | Run the process to export the data. | def run(self):
"""Run the process to export the data."""
while True:
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async_setup_platform | (hass, config, async_add_entities, discovery_info=None) | Set up the Bravia TV platform. | Set up the Bravia TV platform. | async def async_setup_platform(hass, config, async_add_entities, discovery_info=None):
"""Set up the Bravia TV platform."""
host = config[CONF_HOST]
bravia_config_file_path = hass.config.path(BRAVIA_CONFIG_FILE)
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async_setup_entry | (hass, config_entry, async_add_entities) | Add BraviaTV entities from a config_entry. | Add BraviaTV entities from a config_entry. | async def async_setup_entry(hass, config_entry, async_add_entities):
"""Add BraviaTV entities from a config_entry."""
ignored_sources = []
pin = config_entry.data[CONF_PIN]
unique_id = config_entry.unique_id
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BraviaTVDevice.__init__ | (self, client, name, pin, unique_id, device_info, ignored_sources) | Initialize the Bravia TV device. | Initialize the Bravia TV device. | def __init__(self, client, name, pin, unique_id, device_info, ignored_sources):
"""Initialize the Bravia TV device."""
self._pin = pin
self._braviarc = client
self._name = name
self._state = STATE_OFF
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BraviaTVDevice.async_update | (self) | Update TV info. | Update TV info. | async def async_update(self):
"""Update TV info."""
if self._state_lock.locked():
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BraviaTVDevice._get_source | (self) | Return the name of the source. | Return the name of the source. | def _get_source(self):
"""Return the name of the source."""
for key, value in self._content_mapping.items():
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BraviaTVDevice._async_refresh_volume | (self) | Refresh volume information. | Refresh volume information. | async def _async_refresh_volume(self):
"""Refresh volume information."""
volume_info = await self.hass.async_add_executor_job(
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BraviaTVDevice._async_refresh_channels | (self) | Refresh source and channels list. | Refresh source and channels list. | async def _async_refresh_channels(self):
"""Refresh source and channels list."""
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BraviaTVDevice._async_refresh_playing_info | (self) | Refresh Playing information. | Refresh Playing information. | async def _async_refresh_playing_info(self):
"""Refresh Playing information."""
playing_info = await self.hass.async_add_executor_job(
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self._program_name = playing_info.get("programTitle")
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BraviaTVDevice.name | (self) | Return the name of the device. | Return the name of the device. | def name(self):
"""Return the name of the device."""
return self._name | [
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BraviaTVDevice.device_class | (self) | Set the device class to TV. | Set the device class to TV. | def device_class(self):
"""Set the device class to TV."""
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BraviaTVDevice.unique_id | (self) | Return a unique_id for this entity. | Return a unique_id for this entity. | def unique_id(self):
"""Return a unique_id for this entity."""
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BraviaTVDevice.device_info | (self) | Return the device info. | Return the device info. | def device_info(self):
"""Return the device info."""
return self._device_info | [
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BraviaTVDevice.state | (self) | Return the state of the device. | Return the state of the device. | def state(self):
"""Return the state of the device."""
return self._state | [
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BraviaTVDevice.source | (self) | Return the current input source. | Return the current input source. | def source(self):
"""Return the current input source."""
return self._source | [
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BraviaTVDevice.source_list | (self) | List of available input sources. | List of available input sources. | def source_list(self):
"""List of available input sources."""
return self._source_list | [
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BraviaTVDevice.volume_level | (self) | Volume level of the media player (0..1). | Volume level of the media player (0..1). | def volume_level(self):
"""Volume level of the media player (0..1)."""
if self._volume is not None:
return self._volume / 100
return None | [
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BraviaTVDevice.is_volume_muted | (self) | Boolean if volume is currently muted. | Boolean if volume is currently muted. | def is_volume_muted(self):
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BraviaTVDevice.supported_features | (self) | Flag media player features that are supported. | Flag media player features that are supported. | def supported_features(self):
"""Flag media player features that are supported."""
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BraviaTVDevice.media_title | (self) | Title of current playing media. | Title of current playing media. | def media_title(self):
"""Title of current playing media."""
return_value = None
if self._channel_name is not None:
return_value = self._channel_name
if self._program_name is not None:
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BraviaTVDevice.media_content_id | (self) | Content ID of current playing media. | Content ID of current playing media. | def media_content_id(self):
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BraviaTVDevice.media_duration | (self) | Duration of current playing media in seconds. | Duration of current playing media in seconds. | def media_duration(self):
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BraviaTVDevice.set_volume_level | (self, volume) | Set volume level, range 0..1. | Set volume level, range 0..1. | def set_volume_level(self, volume):
"""Set volume level, range 0..1."""
self._braviarc.set_volume_level(volume) | [
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BraviaTVDevice.async_turn_on | (self) | Turn the media player on. | Turn the media player on. | async def async_turn_on(self):
"""Turn the media player on."""
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BraviaTVDevice.async_turn_off | (self) | Turn off media player. | Turn off media player. | async def async_turn_off(self):
"""Turn off media player."""
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BraviaTVDevice.volume_up | (self) | Volume up the media player. | Volume up the media player. | def volume_up(self):
"""Volume up the media player."""
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BraviaTVDevice.volume_down | (self) | Volume down media player. | Volume down media player. | def volume_down(self):
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BraviaTVDevice.mute_volume | (self, mute) | Send mute command. | Send mute command. | def mute_volume(self, mute):
"""Send mute command."""
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BraviaTVDevice.select_source | (self, source) | Set the input source. | Set the input source. | def select_source(self, source):
"""Set the input source."""
if source in self._content_mapping:
uri = self._content_mapping[source]
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BraviaTVDevice.media_play_pause | (self) | Simulate play pause media player. | Simulate play pause media player. | def media_play_pause(self):
"""Simulate play pause media player."""
if self._playing:
self.media_pause()
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BraviaTVDevice.media_play | (self) | Send play command. | Send play command. | def media_play(self):
"""Send play command."""
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BraviaTVDevice.media_pause | (self) | Send media pause command to media player. | Send media pause command to media player. | def media_pause(self):
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BraviaTVDevice.media_stop | (self) | Send media stop command to media player. | Send media stop command to media player. | def media_stop(self):
"""Send media stop command to media player."""
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BraviaTVDevice.media_next_track | (self) | Send next track command. | Send next track command. | def media_next_track(self):
"""Send next track command."""
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BraviaTVDevice.media_previous_track | (self) | Send the previous track command. | Send the previous track command. | def media_previous_track(self):
"""Send the previous track command."""
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test_schema | () | Test schema. | Test schema. | def test_schema():
"""Test schema."""
assert "nl-NL" in tts.SUPPORT_LANGUAGES
processed = tts.PLATFORM_SCHEMA({"platform": "cloud", "language": "nl-NL"})
assert processed["gender"] == "female"
# Should not raise
processed = tts.PLATFORM_SCHEMA(
{"platform": "cloud", "language": "nl-NL"... | [
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get_label_balance | (dataset) |
Given a dataset, return the proportion of each target class and the counts of each class type
Parameters
----------
dataset
Returns
-------
sample_weights, counts
|
Given a dataset, return the proportion of each target class and the counts of each class type | def get_label_balance(dataset):
"""
Given a dataset, return the proportion of each target class and the counts of each class type
Parameters
----------
dataset
Returns
-------
sample_weights, counts
"""
assert hasattr(dataset, 'get_targets')
labels = dataset.get_targets()
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BaseProcess.__init__ | (self, lr=0.001, metrics=None, evaluation_only_metrics=None, l2_weight_decay=0.01, cuda=None, **kwargs) |
Initialization of the Base Trainable object. Any learning procedure that leverages DN3atasets should subclass
this base class.
By default uses the SGD with momentum optimization.
Parameters
----------
cuda : bool, string, None
If boolean, sets whether to... |
Initialization of the Base Trainable object. Any learning procedure that leverages DN3atasets should subclass
this base class. | def __init__(self, lr=0.001, metrics=None, evaluation_only_metrics=None, l2_weight_decay=0.01, cuda=None, **kwargs):
"""
Initialization of the Base Trainable object. Any learning procedure that leverages DN3atasets should subclass
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BaseProcess.set_scheduler | (self, scheduler, step_every_batch=False) |
This allow the addition of a learning rate schedule to the process. By default, a linear warmup with cosine
decay will be used. Any scheduler that is an instance of :any:`Scheduler` (pytorch's schedulers, or extensions
thereof) can be set here. Additionally, a string keywords can be used includ... |
This allow the addition of a learning rate schedule to the process. By default, a linear warmup with cosine
decay will be used. Any scheduler that is an instance of :any:`Scheduler` (pytorch's schedulers, or extensions
thereof) can be set here. Additionally, a string keywords can be used includ... | def set_scheduler(self, scheduler, step_every_batch=False):
"""
This allow the addition of a learning rate schedule to the process. By default, a linear warmup with cosine
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BaseProcess.build_network | (self, **kwargs) |
This method is used to add trainable modules to the process. Rather than placing objects for training
in the __init__ method, they should be placed here.
By default any arguments that propagate unused from __init__ are included here.
|
This method is used to add trainable modules to the process. Rather than placing objects for training
in the __init__ method, they should be placed here. | def build_network(self, **kwargs):
"""
This method is used to add trainable modules to the process. Rather than placing objects for training
in the __init__ method, they should be placed here.
By default any arguments that propagate unused from __init__ are included here.
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BaseProcess.parameters | (self) |
All the trainable parameters in the Trainable. This includes any architecture parameters and meta-parameters.
Returns
-------
params :
An iterator of parameters
|
All the trainable parameters in the Trainable. This includes any architecture parameters and meta-parameters. | def parameters(self):
"""
All the trainable parameters in the Trainable. This includes any architecture parameters and meta-parameters.
Returns
-------
params :
An iterator of parameters
"""
for member in self._trainables:
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BaseProcess.forward | (self, *inputs) |
Given a batch of inputs, return the outputs produced by the trainable module.
Parameters
----------
inputs :
Tensors needed for underlying module.
Returns
-------
outputs :
Outputs of module
|
Given a batch of inputs, return the outputs produced by the trainable module. | def forward(self, *inputs):
"""
Given a batch of inputs, return the outputs produced by the trainable module.
Parameters
----------
inputs :
Tensors needed for underlying module.
Returns
-------
outputs :
Outputs of module
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BaseProcess.calculate_loss | (self, inputs, outputs) |
Given the inputs to and outputs from underlying modules, calculate the loss.
Returns
-------
Loss :
Single loss quantity to be minimized.
|
Given the inputs to and outputs from underlying modules, calculate the loss. | def calculate_loss(self, inputs, outputs):
"""
Given the inputs to and outputs from underlying modules, calculate the loss.
Returns
-------
Loss :
Single loss quantity to be minimized.
"""
if isinstance(outputs, (tuple, list)):
device = o... | [
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BaseProcess.calculate_metrics | (self, inputs, outputs) |
Given the inputs to and outputs from the underlying module. Return tracked metrics.
Parameters
----------
inputs :
Input tensors.
outputs :
Output tensors.
Returns
-------
metrics : OrderedDict, None
Dict... |
Given the inputs to and outputs from the underlying module. Return tracked metrics. | def calculate_metrics(self, inputs, outputs):
"""
Given the inputs to and outputs from the underlying module. Return tracked metrics.
Parameters
----------
inputs :
Input tensors.
outputs :
Output tensors.
Returns
-------
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BaseProcess.evaluate | (self, dataset, **loader_kwargs) |
Calculate and return metrics for a dataset
Parameters
----------
dataset: DN3ataset, DataLoader
The dataset that will be used for evaluation, if not a DataLoader, one will be constructed
loader_kwargs: dict
Args that will be passed to the... |
Calculate and return metrics for a dataset | def evaluate(self, dataset, **loader_kwargs):
"""
Calculate and return metrics for a dataset
Parameters
----------
dataset: DN3ataset, DataLoader
The dataset that will be used for evaluation, if not a DataLoader, one will be constructed
loader_kwargs: di... | [
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BaseProcess.predict | (self, dataset, **loader_kwargs) |
Determine the outputs for all loaded data from the dataset
Parameters
----------
dataset: DN3ataset, DataLoader
The dataset that will be used for evaluation, if not a DataLoader, one will be constructed
loader_kwargs: dict
Args that will ... |
Determine the outputs for all loaded data from the dataset | def predict(self, dataset, **loader_kwargs):
"""
Determine the outputs for all loaded data from the dataset
Parameters
----------
dataset: DN3ataset, DataLoader
The dataset that will be used for evaluation, if not a DataLoader, one will be constructed
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BaseProcess.save_best | (self) |
Create a snapshot of what is being currently trained for re-laoding with the :py:meth:`load_best()` method.
Returns
-------
best : Any
Whatever format is needed for :py:meth:`load_best()`, will be the argument provided to it.
|
Create a snapshot of what is being currently trained for re-laoding with the :py:meth:`load_best()` method. | def save_best(self):
"""
Create a snapshot of what is being currently trained for re-laoding with the :py:meth:`load_best()` method.
Returns
-------
best : Any
Whatever format is needed for :py:meth:`load_best()`, will be the argument provided to it.
"""
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BaseProcess.load_best | (self, best) |
Load the parameters as saved by :py:meth:`save_best()`.
Parameters
----------
best: Any
|
Load the parameters as saved by :py:meth:`save_best()`. | def load_best(self, best):
"""
Load the parameters as saved by :py:meth:`save_best()`.
Parameters
----------
best: Any
"""
for m, state_dict in zip(self._trainables, best):
self.__dict__[m].load_state_dict({k: v.to(self.device) for k, v in state_dict.... | [
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BaseProcess._make_dataloader | (self, dataset, training=False, **loader_kwargs) | Any args that make more sense as a convenience function to be set | Any args that make more sense as a convenience function to be set | def _make_dataloader(self, dataset, training=False, **loader_kwargs):
"""Any args that make more sense as a convenience function to be set"""
if isinstance(dataset, DataLoader):
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return DataLoader(dataset, **self._dataloader_args(dataset, training, **loader_kwargs)) | [
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BaseProcess.fit | (self, training_dataset, epochs=1, validation_dataset=None, step_callback=None,
resume_epoch=None, resume_iteration=None, log_callback=None,
epoch_callback=None, batch_size=8, warmup_frac=0.2, retain_best='loss',
validation_interval=None, train_log_interval=None, **loader_kwargs) |
sklearn/keras-like convenience method to simply proceed with training across multiple epochs of the provided
dataset
Parameters
----------
training_dataset : DN3ataset, DataLoader
validation_dataset : DN3ataset, DataLoader
epochs : int
Total num... |
sklearn/keras-like convenience method to simply proceed with training across multiple epochs of the provided
dataset | def fit(self, training_dataset, epochs=1, validation_dataset=None, step_callback=None,
resume_epoch=None, resume_iteration=None, log_callback=None,
epoch_callback=None, batch_size=8, warmup_frac=0.2, retain_best='loss',
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StandardClassification.fit | (self, training_dataset, epochs=1, validation_dataset=None, step_callback=None, epoch_callback=None,
batch_size=8, warmup_frac=0.2, retain_best='loss', balance_method=None, **loader_kwargs) |
sklearn/keras-like convenience method to simply proceed with training across multiple epochs of the provided
dataset
Parameters
----------
training_dataset : DN3ataset, DataLoader
validation_dataset : DN3ataset, DataLoader
epochs : int
step_callback : ca... |
sklearn/keras-like convenience method to simply proceed with training across multiple epochs of the provided
dataset | def fit(self, training_dataset, epochs=1, validation_dataset=None, step_callback=None, epoch_callback=None,
batch_size=8, warmup_frac=0.2, retain_best='loss', balance_method=None, **loader_kwargs):
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_conf_preprocess | (value) | Preprocess alternative configuration formats. | Preprocess alternative configuration formats. | def _conf_preprocess(value):
"""Preprocess alternative configuration formats."""
if not isinstance(value, dict):
value = {CONF_ENTITIES: value}
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is_on | (hass, entity_id) | Test if the group state is in its ON-state. | Test if the group state is in its ON-state. | def is_on(hass, entity_id):
"""Test if the group state is in its ON-state."""
if REG_KEY not in hass.data:
# Integration not setup yet, it cannot be on
return False
state = hass.states.get(entity_id)
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expand_entity_ids | (hass: HomeAssistantType, entity_ids: Iterable[Any]) | Return entity_ids with group entity ids replaced by their members.
Async friendly.
| Return entity_ids with group entity ids replaced by their members. | def expand_entity_ids(hass: HomeAssistantType, entity_ids: Iterable[Any]) -> List[str]:
"""Return entity_ids with group entity ids replaced by their members.
Async friendly.
"""
found_ids: List[str] = []
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... | [
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get_entity_ids | (
hass: HomeAssistantType, entity_id: str, domain_filter: Optional[str] = None
) | Get members of this group.
Async friendly.
| Get members of this group. | def get_entity_ids(
hass: HomeAssistantType, entity_id: str, domain_filter: Optional[str] = None
) -> List[str]:
"""Get members of this group.
Async friendly.
"""
group = hass.states.get(entity_id)
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groups_with_entity | (hass: HomeAssistantType, entity_id: str) | Get all groups that contain this entity.
Async friendly.
| Get all groups that contain this entity. | def groups_with_entity(hass: HomeAssistantType, entity_id: str) -> List[str]:
"""Get all groups that contain this entity.
Async friendly.
"""
if DOMAIN not in hass.data:
return []
groups = []
for group in hass.data[DOMAIN].entities:
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async_setup | (hass, config) | Set up all groups found defined in the configuration. | Set up all groups found defined in the configuration. | async def async_setup(hass, config):
"""Set up all groups found defined in the configuration."""
component = hass.data.get(DOMAIN)
if component is None:
component = hass.data[DOMAIN] = EntityComponent(_LOGGER, DOMAIN, hass)
hass.data[REG_KEY] = GroupIntegrationRegistry()
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_process_group_platform | (hass, domain, platform) | Process a group platform. | Process a group platform. | async def _process_group_platform(hass, domain, platform):
"""Process a group platform."""
current_domain.set(domain)
platform.async_describe_on_off_states(hass, hass.data[REG_KEY]) | [
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_async_process_config | (hass, config, component) | Process group configuration. | Process group configuration. | async def _async_process_config(hass, config, component):
"""Process group configuration."""
hass.data.setdefault(GROUP_ORDER, 0)
tasks = []
for object_id, conf in config.get(DOMAIN, {}).items():
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GroupIntegrationRegistry.exclude_domain | (self) | Exclude the current domain. | Exclude the current domain. | def exclude_domain(self) -> None:
"""Exclude the current domain."""
self.exclude_domains.add(current_domain.get()) | [
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GroupIntegrationRegistry.on_off_states | (self, on_states: Set, off_state: str) | Register on and off states for the current domain. | Register on and off states for the current domain. | def on_off_states(self, on_states: Set, off_state: str) -> None:
"""Register on and off states for the current domain."""
for on_state in on_states:
if on_state not in self.on_off_mapping:
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GroupEntity.should_poll | (self) | Disable polling for group. | Disable polling for group. | def should_poll(self) -> bool:
"""Disable polling for group."""
return False | [
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GroupEntity.async_added_to_hass | (self) | Register listeners. | Register listeners. | async def async_added_to_hass(self) -> None:
"""Register listeners."""
assert self.hass is not None
async def _update_at_start(_):
await self.async_update()
self.async_write_ha_state()
self.hass.bus.async_listen_once(EVENT_HOMEASSISTANT_START, _update_at_start) | [
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GroupEntity.async_defer_or_update_ha_state | (self) | Only update once at start. | Only update once at start. | async def async_defer_or_update_ha_state(self) -> None:
"""Only update once at start."""
assert self.hass is not None
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GroupEntity.async_update | (self) | Abstract method to update the entity. | Abstract method to update the entity. | async def async_update(self) -> None:
"""Abstract method to update the entity.""" | [
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Group.__init__ | (
self,
hass,
name,
order=None,
icon=None,
user_defined=True,
entity_ids=None,
mode=None,
) | Initialize a group.
This Object has factory function for creation.
| Initialize a group. | def __init__(
self,
hass,
name,
order=None,
icon=None,
user_defined=True,
entity_ids=None,
mode=None,
):
"""Initialize a group.
This Object has factory function for creation.
"""
self.hass = hass
self._name = na... | [
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Group.create_group | (
hass,
name,
entity_ids=None,
user_defined=True,
icon=None,
object_id=None,
mode=None,
order=None,
) | Initialize a group. | Initialize a group. | def create_group(
hass,
name,
entity_ids=None,
user_defined=True,
icon=None,
object_id=None,
mode=None,
order=None,
):
"""Initialize a group."""
return asyncio.run_coroutine_threadsafe(
Group.async_create_group(
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Group.async_create_group | (
hass,
name,
entity_ids=None,
user_defined=True,
icon=None,
object_id=None,
mode=None,
order=None,
) | Initialize a group.
This method must be run in the event loop.
| Initialize a group. | async def async_create_group(
hass,
name,
entity_ids=None,
user_defined=True,
icon=None,
object_id=None,
mode=None,
order=None,
):
"""Initialize a group.
This method must be run in the event loop.
"""
if order is None:
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Group.should_poll | (self) | No need to poll because groups will update themselves. | No need to poll because groups will update themselves. | def should_poll(self):
"""No need to poll because groups will update themselves."""
return False | [
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Group.name | (self) | Return the name of the group. | Return the name of the group. | def name(self):
"""Return the name of the group."""
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Group.name | (self, value) | Set Group name. | Set Group name. | def name(self, value):
"""Set Group name."""
self._name = value | [
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Group.state | (self) | Return the state of the group. | Return the state of the group. | def state(self):
"""Return the state of the group."""
return self._state | [
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Group.icon | (self) | Return the icon of the group. | Return the icon of the group. | def icon(self):
"""Return the icon of the group."""
return self._icon | [
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Group.icon | (self, value) | Set Icon for group. | Set Icon for group. | def icon(self, value):
"""Set Icon for group."""
self._icon = value | [
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4
] | [
548,
26
] | python | en | ['en', 'en', 'en'] | True |
Group.state_attributes | (self) | Return the state attributes for the group. | Return the state attributes for the group. | def state_attributes(self):
"""Return the state attributes for the group."""
data = {ATTR_ENTITY_ID: self.tracking, ATTR_ORDER: self._order}
if not self.user_defined:
data[ATTR_AUTO] = True
return data | [
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Group.assumed_state | (self) | Test if any member has an assumed state. | Test if any member has an assumed state. | def assumed_state(self):
"""Test if any member has an assumed state."""
return self._assumed_state | [
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Group.update_tracked_entity_ids | (self, entity_ids) | Update the member entity IDs. | Update the member entity IDs. | def update_tracked_entity_ids(self, entity_ids):
"""Update the member entity IDs."""
asyncio.run_coroutine_threadsafe(
self.async_update_tracked_entity_ids(entity_ids), self.hass.loop
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Group.async_update_tracked_entity_ids | (self, entity_ids) | Update the member entity IDs.
This method must be run in the event loop.
| Update the member entity IDs. | async def async_update_tracked_entity_ids(self, entity_ids):
"""Update the member entity IDs.
This method must be run in the event loop.
"""
self._async_stop()
self._set_tracked(entity_ids)
self._reset_tracked_state()
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Group._set_tracked | (self, entity_ids) | Tuple of entities to be tracked. | Tuple of entities to be tracked. | def _set_tracked(self, entity_ids):
"""Tuple of entities to be tracked."""
# tracking are the entities we want to track
# trackable are the entities we actually watch
if not entity_ids:
self.tracking = ()
self.trackable = ()
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580,
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Group._async_start | (self, *_) | Start tracking members and write state. | Start tracking members and write state. | def _async_start(self, *_):
"""Start tracking members and write state."""
self._reset_tracked_state()
self._async_start_tracking()
self.async_write_ha_state() | [
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] | python | en | ['en', 'en', 'en'] | True |
Group._async_start_tracking | (self) | Start tracking members.
This method must be run in the event loop.
| Start tracking members. | def _async_start_tracking(self):
"""Start tracking members.
This method must be run in the event loop.
"""
if self.trackable and self._async_unsub_state_changed is None:
self._async_unsub_state_changed = async_track_state_change_event(
self.hass, self.trackab... | [
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] | [
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] | python | en | ['en', 'sn', 'en'] | True |
Group._async_stop | (self) | Unregister the group from Home Assistant.
This method must be run in the event loop.
| Unregister the group from Home Assistant. | def _async_stop(self):
"""Unregister the group from Home Assistant.
This method must be run in the event loop.
"""
if self._async_unsub_state_changed:
self._async_unsub_state_changed()
self._async_unsub_state_changed = None | [
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] | [
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] | python | en | ['en', 'en', 'en'] | True |
Group.async_update | (self) | Query all members and determine current group state. | Query all members and determine current group state. | async def async_update(self):
"""Query all members and determine current group state."""
self._state = None
self._async_update_group_state() | [
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637,
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Group.async_added_to_hass | (self) | Handle addition to Home Assistant. | Handle addition to Home Assistant. | async def async_added_to_hass(self):
"""Handle addition to Home Assistant."""
if self.hass.state != CoreState.running:
self.hass.bus.async_listen_once(
EVENT_HOMEASSISTANT_START, self._async_start
)
return
if self.tracking:
self._r... | [
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Group.async_will_remove_from_hass | (self) | Handle removal from Home Assistant. | Handle removal from Home Assistant. | async def async_will_remove_from_hass(self):
"""Handle removal from Home Assistant."""
self._async_stop() | [
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Group._async_state_changed_listener | (self, event) | Respond to a member state changing.
This method must be run in the event loop.
| Respond to a member state changing. | async def _async_state_changed_listener(self, event):
"""Respond to a member state changing.
This method must be run in the event loop.
"""
# removed
if self._async_unsub_state_changed is None:
return
self.async_set_context(event.context)
new_state =... | [
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"="... | [
655,
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] | [
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] | python | en | ['en', 'en', 'en'] | True |
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