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stringlengths 1
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stringlengths 0
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stringlengths 4
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stringlengths 2
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⌀ | embed_func_code
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725,045 | tf_keras.src.engine.training | test_on_batch | Test the model on a single batch of samples.
Args:
x: Input data. It could be:
- A Numpy array (or array-like), or a list of arrays (in case the
model has multiple inputs).
- A TensorFlow tensor, or a list of tensors (in case the model has
multiple inputs).
- A dict mapping input names to the corresponding array/tensors,
if the model has named inputs.
y: Target data. Like the input data `x`, it could be either Numpy
array(s) or TensorFlow tensor(s). It should be consistent with `x`
(you cannot have Numpy inputs and tensor targets, or inversely).
sample_weight: Optional array of the same length as x, containing
weights to apply to the model's loss for each sample. In the case
of temporal data, you can pass a 2D array with shape (samples,
sequence_length), to apply a different weight to every timestep of
every sample.
reset_metrics: If `True`, the metrics returned will be only for this
batch. If `False`, the metrics will be statefully accumulated
across batches.
return_dict: If `True`, loss and metric results are returned as a
dict, with each key being the name of the metric. If `False`, they
are returned as a list.
Returns:
Scalar test loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute `model.metrics_names` will give you
the display labels for the scalar outputs.
Raises:
RuntimeError: If `model.test_on_batch` is wrapped in a
`tf.function`.
| def test_on_batch(
self,
x,
y=None,
sample_weight=None,
reset_metrics=True,
return_dict=False,
):
"""Test the model on a single batch of samples.
Args:
x: Input data. It could be:
- A Numpy array (or array-like), or a list of arrays (in case the
model has multiple inputs).
- A TensorFlow tensor, or a list of tensors (in case the model has
multiple inputs).
- A dict mapping input names to the corresponding array/tensors,
if the model has named inputs.
y: Target data. Like the input data `x`, it could be either Numpy
array(s) or TensorFlow tensor(s). It should be consistent with `x`
(you cannot have Numpy inputs and tensor targets, or inversely).
sample_weight: Optional array of the same length as x, containing
weights to apply to the model's loss for each sample. In the case
of temporal data, you can pass a 2D array with shape (samples,
sequence_length), to apply a different weight to every timestep of
every sample.
reset_metrics: If `True`, the metrics returned will be only for this
batch. If `False`, the metrics will be statefully accumulated
across batches.
return_dict: If `True`, loss and metric results are returned as a
dict, with each key being the name of the metric. If `False`, they
are returned as a list.
Returns:
Scalar test loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute `model.metrics_names` will give you
the display labels for the scalar outputs.
Raises:
RuntimeError: If `model.test_on_batch` is wrapped in a
`tf.function`.
"""
self._assert_compile_was_called()
self._check_call_args("test_on_batch")
_disallow_inside_tf_function("test_on_batch")
if reset_metrics:
self.reset_metrics()
with self.distribute_strategy.scope():
iterator = data_adapter.single_batch_iterator(
self.distribute_strategy, x, y, sample_weight
)
self.test_function = self.make_test_function()
logs = self.test_function(iterator)
logs = tf_utils.sync_to_numpy_or_python_type(logs)
if return_dict:
return logs
else:
return flatten_metrics_in_order(logs, self.metrics_names)
| (self, x, y=None, sample_weight=None, reset_metrics=True, return_dict=False) | [
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] |
725,046 | tf_keras.src.engine.training | test_step | The logic for one evaluation step.
This method can be overridden to support custom evaluation logic.
This method is called by `Model.make_test_function`.
This function should contain the mathematical logic for one step of
evaluation.
This typically includes the forward pass, loss calculation, and metrics
updates.
Configuration details for *how* this logic is run (e.g. `tf.function`
and `tf.distribute.Strategy` settings), should be left to
`Model.make_test_function`, which can also be overridden.
Args:
data: A nested structure of `Tensor`s.
Returns:
A `dict` containing values that will be passed to
`tf.keras.callbacks.CallbackList.on_train_batch_end`. Typically, the
values of the `Model`'s metrics are returned.
| def test_step(self, data):
"""The logic for one evaluation step.
This method can be overridden to support custom evaluation logic.
This method is called by `Model.make_test_function`.
This function should contain the mathematical logic for one step of
evaluation.
This typically includes the forward pass, loss calculation, and metrics
updates.
Configuration details for *how* this logic is run (e.g. `tf.function`
and `tf.distribute.Strategy` settings), should be left to
`Model.make_test_function`, which can also be overridden.
Args:
data: A nested structure of `Tensor`s.
Returns:
A `dict` containing values that will be passed to
`tf.keras.callbacks.CallbackList.on_train_batch_end`. Typically, the
values of the `Model`'s metrics are returned.
"""
x, y, sample_weight = data_adapter.unpack_x_y_sample_weight(data)
y_pred = self(x, training=False)
# Updates stateful loss metrics.
self.compute_loss(x, y, y_pred, sample_weight)
return self.compute_metrics(x, y, y_pred, sample_weight)
| (self, data) | [
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725,047 | tf_keras.src.engine.training | to_json | Returns a JSON string containing the network configuration.
To load a network from a JSON save file, use
`keras.models.model_from_json(json_string, custom_objects={})`.
Args:
**kwargs: Additional keyword arguments to be passed to
*`json.dumps()`.
Returns:
A JSON string.
| def to_json(self, **kwargs):
"""Returns a JSON string containing the network configuration.
To load a network from a JSON save file, use
`keras.models.model_from_json(json_string, custom_objects={})`.
Args:
**kwargs: Additional keyword arguments to be passed to
*`json.dumps()`.
Returns:
A JSON string.
"""
model_config = self._updated_config()
return json.dumps(
model_config, default=json_utils.get_json_type, **kwargs
)
| (self, **kwargs) | [
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725,048 | tf_keras.src.engine.training | to_yaml | Returns a yaml string containing the network configuration.
Note: Since TF 2.6, this method is no longer supported and will raise a
RuntimeError.
To load a network from a yaml save file, use
`keras.models.model_from_yaml(yaml_string, custom_objects={})`.
`custom_objects` should be a dictionary mapping
the names of custom losses / layers / etc to the corresponding
functions / classes.
Args:
**kwargs: Additional keyword arguments
to be passed to `yaml.dump()`.
Returns:
A YAML string.
Raises:
RuntimeError: announces that the method poses a security risk
| def to_yaml(self, **kwargs):
"""Returns a yaml string containing the network configuration.
Note: Since TF 2.6, this method is no longer supported and will raise a
RuntimeError.
To load a network from a yaml save file, use
`keras.models.model_from_yaml(yaml_string, custom_objects={})`.
`custom_objects` should be a dictionary mapping
the names of custom losses / layers / etc to the corresponding
functions / classes.
Args:
**kwargs: Additional keyword arguments
to be passed to `yaml.dump()`.
Returns:
A YAML string.
Raises:
RuntimeError: announces that the method poses a security risk
"""
raise RuntimeError(
"Method `model.to_yaml()` has been removed due to security risk of "
"arbitrary code execution. Please use `model.to_json()` instead."
)
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] |
725,049 | tf_keras.src.engine.training | train_on_batch | Runs a single gradient update on a single batch of data.
Args:
x: Input data. It could be:
- A Numpy array (or array-like), or a list of arrays
(in case the model has multiple inputs).
- A TensorFlow tensor, or a list of tensors
(in case the model has multiple inputs).
- A dict mapping input names to the corresponding array/tensors,
if the model has named inputs.
y: Target data. Like the input data `x`, it could be either Numpy
array(s) or TensorFlow tensor(s).
sample_weight: Optional array of the same length as x, containing
weights to apply to the model's loss for each sample. In the case
of temporal data, you can pass a 2D array with shape (samples,
sequence_length), to apply a different weight to every timestep of
every sample.
class_weight: Optional dictionary mapping class indices (integers)
to a weight (float) to apply to the model's loss for the samples
from this class during training. This can be useful to tell the
model to "pay more attention" to samples from an under-represented
class. When `class_weight` is specified and targets have a rank of
2 or greater, either `y` must be one-hot encoded, or an explicit
final dimension of `1` must be included for sparse class labels.
reset_metrics: If `True`, the metrics returned will be only for this
batch. If `False`, the metrics will be statefully accumulated
across batches.
return_dict: If `True`, loss and metric results are returned as a
dict, with each key being the name of the metric. If `False`, they
are returned as a list.
Returns:
Scalar training loss
(if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute `model.metrics_names` will give you
the display labels for the scalar outputs.
Raises:
RuntimeError: If `model.train_on_batch` is wrapped in a `tf.function`.
| def train_on_batch(
self,
x,
y=None,
sample_weight=None,
class_weight=None,
reset_metrics=True,
return_dict=False,
):
"""Runs a single gradient update on a single batch of data.
Args:
x: Input data. It could be:
- A Numpy array (or array-like), or a list of arrays
(in case the model has multiple inputs).
- A TensorFlow tensor, or a list of tensors
(in case the model has multiple inputs).
- A dict mapping input names to the corresponding array/tensors,
if the model has named inputs.
y: Target data. Like the input data `x`, it could be either Numpy
array(s) or TensorFlow tensor(s).
sample_weight: Optional array of the same length as x, containing
weights to apply to the model's loss for each sample. In the case
of temporal data, you can pass a 2D array with shape (samples,
sequence_length), to apply a different weight to every timestep of
every sample.
class_weight: Optional dictionary mapping class indices (integers)
to a weight (float) to apply to the model's loss for the samples
from this class during training. This can be useful to tell the
model to "pay more attention" to samples from an under-represented
class. When `class_weight` is specified and targets have a rank of
2 or greater, either `y` must be one-hot encoded, or an explicit
final dimension of `1` must be included for sparse class labels.
reset_metrics: If `True`, the metrics returned will be only for this
batch. If `False`, the metrics will be statefully accumulated
across batches.
return_dict: If `True`, loss and metric results are returned as a
dict, with each key being the name of the metric. If `False`, they
are returned as a list.
Returns:
Scalar training loss
(if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute `model.metrics_names` will give you
the display labels for the scalar outputs.
Raises:
RuntimeError: If `model.train_on_batch` is wrapped in a `tf.function`.
"""
self._assert_compile_was_called()
self._check_call_args("train_on_batch")
_disallow_inside_tf_function("train_on_batch")
if reset_metrics:
self.reset_metrics()
with self.distribute_strategy.scope(), training_utils.RespectCompiledTrainableState( # noqa: E501
self
):
iterator = data_adapter.single_batch_iterator(
self.distribute_strategy, x, y, sample_weight, class_weight
)
self.train_function = self.make_train_function()
logs = self.train_function(iterator)
logs = tf_utils.sync_to_numpy_or_python_type(logs)
if return_dict:
return logs
else:
return flatten_metrics_in_order(logs, self.metrics_names)
| (self, x, y=None, sample_weight=None, class_weight=None, reset_metrics=True, return_dict=False) | [
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725,050 | tf_keras.src.engine.training | train_step | The logic for one training step.
This method can be overridden to support custom training logic.
For concrete examples of how to override this method see
[Customizing what happens in fit](
https://www.tensorflow.org/guide/keras/customizing_what_happens_in_fit).
This method is called by `Model.make_train_function`.
This method should contain the mathematical logic for one step of
training. This typically includes the forward pass, loss calculation,
backpropagation, and metric updates.
Configuration details for *how* this logic is run (e.g. `tf.function`
and `tf.distribute.Strategy` settings), should be left to
`Model.make_train_function`, which can also be overridden.
Args:
data: A nested structure of `Tensor`s.
Returns:
A `dict` containing values that will be passed to
`tf.keras.callbacks.CallbackList.on_train_batch_end`. Typically, the
values of the `Model`'s metrics are returned. Example:
`{'loss': 0.2, 'accuracy': 0.7}`.
| def train_step(self, data):
"""The logic for one training step.
This method can be overridden to support custom training logic.
For concrete examples of how to override this method see
[Customizing what happens in fit](
https://www.tensorflow.org/guide/keras/customizing_what_happens_in_fit).
This method is called by `Model.make_train_function`.
This method should contain the mathematical logic for one step of
training. This typically includes the forward pass, loss calculation,
backpropagation, and metric updates.
Configuration details for *how* this logic is run (e.g. `tf.function`
and `tf.distribute.Strategy` settings), should be left to
`Model.make_train_function`, which can also be overridden.
Args:
data: A nested structure of `Tensor`s.
Returns:
A `dict` containing values that will be passed to
`tf.keras.callbacks.CallbackList.on_train_batch_end`. Typically, the
values of the `Model`'s metrics are returned. Example:
`{'loss': 0.2, 'accuracy': 0.7}`.
"""
x, y, sample_weight = data_adapter.unpack_x_y_sample_weight(data)
# Run forward pass.
with tf.GradientTape() as tape:
y_pred = self(x, training=True)
loss = self.compute_loss(x, y, y_pred, sample_weight)
self._validate_target_and_loss(y, loss)
# Run backwards pass.
self.optimizer.minimize(loss, self.trainable_variables, tape=tape)
return self.compute_metrics(x, y, y_pred, sample_weight)
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] |
725,051 | tf_keras.src.engine.sequential | Sequential | `Sequential` groups a linear stack of layers into a `tf.keras.Model`.
`Sequential` provides training and inference features on this model.
Examples:
```python
model = tf.keras.Sequential()
model.add(tf.keras.Input(shape=(16,)))
model.add(tf.keras.layers.Dense(8))
# Note that you can also omit the initial `Input`.
# In that case the model doesn't have any weights until the first call
# to a training/evaluation method (since it isn't yet built):
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(8))
model.add(tf.keras.layers.Dense(4))
# model.weights not created yet
# Whereas if you specify an `Input`, the model gets built
# continuously as you are adding layers:
model = tf.keras.Sequential()
model.add(tf.keras.Input(shape=(16,)))
model.add(tf.keras.layers.Dense(4))
len(model.weights)
# Returns "2"
# When using the delayed-build pattern (no input shape specified), you can
# choose to manually build your model by calling
# `build(batch_input_shape)`:
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(8))
model.add(tf.keras.layers.Dense(4))
model.build((None, 16))
len(model.weights)
# Returns "4"
# Note that when using the delayed-build pattern (no input shape specified),
# the model gets built the first time you call `fit`, `eval`, or `predict`,
# or the first time you call the model on some input data.
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(8))
model.add(tf.keras.layers.Dense(1))
model.compile(optimizer='sgd', loss='mse')
# This builds the model for the first time:
model.fit(x, y, batch_size=32, epochs=10)
```
| class Sequential(functional.Functional):
"""`Sequential` groups a linear stack of layers into a `tf.keras.Model`.
`Sequential` provides training and inference features on this model.
Examples:
```python
model = tf.keras.Sequential()
model.add(tf.keras.Input(shape=(16,)))
model.add(tf.keras.layers.Dense(8))
# Note that you can also omit the initial `Input`.
# In that case the model doesn't have any weights until the first call
# to a training/evaluation method (since it isn't yet built):
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(8))
model.add(tf.keras.layers.Dense(4))
# model.weights not created yet
# Whereas if you specify an `Input`, the model gets built
# continuously as you are adding layers:
model = tf.keras.Sequential()
model.add(tf.keras.Input(shape=(16,)))
model.add(tf.keras.layers.Dense(4))
len(model.weights)
# Returns "2"
# When using the delayed-build pattern (no input shape specified), you can
# choose to manually build your model by calling
# `build(batch_input_shape)`:
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(8))
model.add(tf.keras.layers.Dense(4))
model.build((None, 16))
len(model.weights)
# Returns "4"
# Note that when using the delayed-build pattern (no input shape specified),
# the model gets built the first time you call `fit`, `eval`, or `predict`,
# or the first time you call the model on some input data.
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(8))
model.add(tf.keras.layers.Dense(1))
model.compile(optimizer='sgd', loss='mse')
# This builds the model for the first time:
model.fit(x, y, batch_size=32, epochs=10)
```
"""
@tf.__internal__.tracking.no_automatic_dependency_tracking
@traceback_utils.filter_traceback
def __init__(self, layers=None, name=None):
"""Creates a `Sequential` model instance.
Args:
layers: Optional list of layers to add to the model.
name: Optional name for the model.
"""
# Skip the init in FunctionalModel since model doesn't have input/output
# yet
super(functional.Functional, self).__init__(name=name, autocast=False)
self.supports_masking = True
self._compute_output_and_mask_jointly = True
self._auto_track_sub_layers = False
self._inferred_input_shape = None
self._has_explicit_input_shape = False
self._input_dtype = None
self._layer_call_argspecs = {}
self._created_nodes = set()
# Flag that indicate whether the sequential network topology has been
# created. It is false when there isn't any layer, or the layers don't
# have an input shape.
self._graph_initialized = False
# Unfortunately some Sequential models using custom layers or
# FeatureColumn layers have multiple inputs. This is fundamentally
# incompatible with most of the Sequential API, and we have to disable a
# number of features for such models.
self._use_legacy_deferred_behavior = False
# Add to the model any layers passed to the constructor.
if layers:
if not isinstance(layers, (list, tuple)):
layers = [layers]
for layer in layers:
self.add(layer)
@property
def layers(self):
# Historically, `sequential.layers` only returns layers that were added
# via `add`, and omits the auto-generated `InputLayer` that comes at the
# bottom of the stack.
# `Trackable` manages the `_layers` attributes and does filtering
# over it.
layers = super().layers
if layers and isinstance(layers[0], input_layer.InputLayer):
return layers[1:]
return layers[:]
@tf.__internal__.tracking.no_automatic_dependency_tracking
@traceback_utils.filter_traceback
def add(self, layer):
"""Adds a layer instance on top of the layer stack.
Args:
layer: layer instance.
Raises:
TypeError: If `layer` is not a layer instance.
ValueError: In case the `layer` argument does not
know its input shape.
ValueError: In case the `layer` argument has
multiple output tensors, or is already connected
somewhere else (forbidden in `Sequential` models).
"""
# If we are passed a TF-Keras tensor created by keras.Input(), we can
# extract the input layer from its keras history and use that without
# any loss of
# generality.
if hasattr(layer, "_keras_history"):
origin_layer = layer._keras_history[0]
if isinstance(origin_layer, input_layer.InputLayer):
layer = origin_layer
if isinstance(layer, tf.Module):
if not isinstance(layer, base_layer.Layer):
layer = functional.ModuleWrapper(layer)
else:
raise TypeError(
"The added layer must be an instance of class Layer. "
f"Received: layer={layer} of type {type(layer)}."
)
tf_utils.assert_no_legacy_layers([layer])
if not self._is_layer_name_unique(layer):
raise ValueError(
"All layers added to a Sequential model "
f'should have unique names. Name "{layer.name}" is already '
"the name of a layer in this model. Update the `name` argument "
"to pass a unique name."
)
self.built = False
set_inputs = False
self._maybe_create_attribute("_self_tracked_trackables", [])
if not self._self_tracked_trackables:
if isinstance(layer, input_layer.InputLayer):
# Case where the user passes an Input or InputLayer layer via
# `add`.
set_inputs = True
else:
batch_shape, dtype = training_utils.get_input_shape_and_dtype(
layer
)
if batch_shape:
# Instantiate an input layer.
x = input_layer.Input(
batch_shape=batch_shape,
dtype=dtype,
name=layer.name + "_input",
)
# This will build the current layer
# and create the node connecting the current layer
# to the input layer we just created.
layer(x)
set_inputs = True
if set_inputs:
outputs = tf.nest.flatten(layer._inbound_nodes[-1].outputs)
if len(outputs) != 1:
raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG)
self.outputs = outputs
self.inputs = layer_utils.get_source_inputs(self.outputs[0])
self.built = True
self._has_explicit_input_shape = True
elif self.outputs:
# If the model is being built continuously on top of an input layer:
# refresh its output.
output_tensor = layer(self.outputs[0])
if len(tf.nest.flatten(output_tensor)) != 1:
raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG)
self.outputs = [output_tensor]
self.built = True
if set_inputs or self._graph_initialized:
self._init_graph_network(self.inputs, self.outputs)
self._graph_initialized = True
else:
self._self_tracked_trackables.append(layer)
self._handle_deferred_layer_dependencies([layer])
self._layer_call_argspecs[layer] = tf_inspect.getfullargspec(layer.call)
@tf.__internal__.tracking.no_automatic_dependency_tracking
@traceback_utils.filter_traceback
def pop(self):
"""Removes the last layer in the model.
Raises:
TypeError: if there are no layers in the model.
"""
if not self.layers:
raise Ty | (layers=None, name=None) | [
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] |
725,057 | tf_keras.src.engine.sequential | __init__ | Creates a `Sequential` model instance.
Args:
layers: Optional list of layers to add to the model.
name: Optional name for the model.
| @tf.__internal__.tracking.no_automatic_dependency_tracking
@traceback_utils.filter_traceback
def add(self, layer):
"""Adds a layer instance on top of the layer stack.
Args:
layer: layer instance.
Raises:
TypeError: If `layer` is not a layer instance.
ValueError: In case the `layer` argument does not
know its input shape.
ValueError: In case the `layer` argument has
multiple output tensors, or is already connected
somewhere else (forbidden in `Sequential` models).
"""
# If we are passed a TF-Keras tensor created by keras.Input(), we can
# extract the input layer from its keras history and use that without
# any loss of
# generality.
if hasattr(layer, "_keras_history"):
origin_layer = layer._keras_history[0]
if isinstance(origin_layer, input_layer.InputLayer):
layer = origin_layer
if isinstance(layer, tf.Module):
if not isinstance(layer, base_layer.Layer):
layer = functional.ModuleWrapper(layer)
else:
raise TypeError(
"The added layer must be an instance of class Layer. "
f"Received: layer={layer} of type {type(layer)}."
)
tf_utils.assert_no_legacy_layers([layer])
if not self._is_layer_name_unique(layer):
raise ValueError(
"All layers added to a Sequential model "
f'should have unique names. Name "{layer.name}" is already '
"the name of a layer in this model. Update the `name` argument "
"to pass a unique name."
)
self.built = False
set_inputs = False
self._maybe_create_attribute("_self_tracked_trackables", [])
if not self._self_tracked_trackables:
if isinstance(layer, input_layer.InputLayer):
# Case where the user passes an Input or InputLayer layer via
# `add`.
set_inputs = True
else:
batch_shape, dtype = training_utils.get_input_shape_and_dtype(
layer
)
if batch_shape:
# Instantiate an input layer.
x = input_layer.Input(
batch_shape=batch_shape,
dtype=dtype,
name=layer.name + "_input",
)
# This will build the current layer
# and create the node connecting the current layer
# to the input layer we just created.
layer(x)
set_inputs = True
if set_inputs:
outputs = tf.nest.flatten(layer._inbound_nodes[-1].outputs)
if len(outputs) != 1:
raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG)
self.outputs = outputs
self.inputs = layer_utils.get_source_inputs(self.outputs[0])
self.built = True
self._has_explicit_input_shape = True
elif self.outputs:
# If the model is being built continuously on top of an input layer:
# refresh its output.
output_tensor = layer(self.outputs[0])
if len(tf.nest.flatten(output_tensor)) != 1:
raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG)
self.outputs = [output_tensor]
self.built = True
if set_inputs or self._graph_initialized:
self._init_graph_network(self.inputs, self.outputs)
self._graph_initialized = True
else:
self._self_tracked_trackables.append(layer)
self._handle_deferred_layer_dependencies([layer])
self._layer_call_argspecs[layer] = tf_inspect.getfullargspec(layer.call)
| (self, layers=None, name=None) | [
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725,067 | tf_keras.src.engine.sequential | _assert_weights_created | null | def _assert_weights_created(self):
if self._graph_initialized:
return
# When the graph has not been initialized, use the Model's
# implementation to to check if the weights has been created.
super(functional.Functional, self)._assert_weights_created()
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] |
725,069 | tf_keras.src.engine.sequential | _build_graph_network_for_inferred_shape | @tf.__internal__.tracking.no_automatic_dependency_tracking
@traceback_utils.filter_traceback
def add(self, layer):
"""Adds a layer instance on top of the layer stack.
Args:
layer: layer instance.
Raises:
TypeError: If `layer` is not a layer instance.
ValueError: In case the `layer` argument does not
know its input shape.
ValueError: In case the `layer` argument has
multiple output tensors, or is already connected
somewhere else (forbidden in `Sequential` models).
"""
# If we are passed a TF-Keras tensor created by keras.Input(), we can
# extract the input layer from its keras history and use that without
# any loss of
# generality.
if hasattr(layer, "_keras_history"):
origin_layer = layer._keras_history[0]
if isinstance(origin_layer, input_layer.InputLayer):
layer = origin_layer
if isinstance(layer, tf.Module):
if not isinstance(layer, base_layer.Layer):
layer = functional.ModuleWrapper(layer)
else:
raise TypeError(
"The added layer must be an instance of class Layer. "
f"Received: layer={layer} of type {type(layer)}."
)
tf_utils.assert_no_legacy_layers([layer])
if not self._is_layer_name_unique(layer):
raise ValueError(
"All layers added to a Sequential model "
f'should have unique names. Name "{layer.name}" is already '
"the name of a layer in this model. Update the `name` argument "
"to pass a unique name."
)
self.built = False
set_inputs = False
self._maybe_create_attribute("_self_tracked_trackables", [])
if not self._self_tracked_trackables:
if isinstance(layer, input_layer.InputLayer):
# Case where the user passes an Input or InputLayer layer via
# `add`.
set_inputs = True
else:
batch_shape, dtype = training_utils.get_input_shape_and_dtype(
layer
)
if batch_shape:
# Instantiate an input layer.
x = input_layer.Input(
batch_shape=batch_shape,
dtype=dtype,
name=layer.name + "_input",
)
# This will build the current layer
# and create the node connecting the current layer
# to the input layer we just created.
layer(x)
set_inputs = True
if set_inputs:
outputs = tf.nest.flatten(layer._inbound_nodes[-1].outputs)
if len(outputs) != 1:
raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG)
self.outputs = outputs
self.inputs = layer_utils.get_source_inputs(self.outputs[0])
self.built = True
self._has_explicit_input_shape = True
elif self.outputs:
# If the model is being built continuously on top of an input layer:
# refresh its output.
output_tensor = layer(self.outputs[0])
if len(tf.nest.flatten(output_tensor)) != 1:
raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG)
self.outputs = [output_tensor]
self.built = True
if set_inputs or self._graph_initialized:
self._init_graph_network(self.inputs, self.outputs)
self._graph_initialized = True
else:
self._self_tracked_trackables.append(layer)
self._handle_deferred_layer_dependencies([layer])
self._layer_call_argspecs[layer] = tf_inspect.getfullargspec(layer.call)
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|
725,074 | tf_keras.src.engine.functional | _compute_tensor_usage_count | Compute the #. of tensor usages for all the output tensors of layers.
The computed tensor usage count is saved as `self._tensor_usage_count`.
This is later used for saving memory in eager computation by releasing
no-longer-needed tensors as early as possible.
| def _compute_tensor_usage_count(self):
"""Compute the #. of tensor usages for all the output tensors of layers.
The computed tensor usage count is saved as `self._tensor_usage_count`.
This is later used for saving memory in eager computation by releasing
no-longer-needed tensors as early as possible.
"""
tensor_usage_count = collections.Counter()
available_tensors = set(str(id(tensor)) for tensor in self.inputs)
depth_keys = list(self._nodes_by_depth.keys())
depth_keys.sort(reverse=True)
depth_keys = depth_keys[1:]
for depth in depth_keys:
for node in self._nodes_by_depth[depth]:
input_tensors = {
str(id(tensor))
for tensor in tf.nest.flatten(node.keras_inputs)
}
if input_tensors.issubset(available_tensors):
for tensor in tf.nest.flatten(node.keras_inputs):
tensor_usage_count[str(id(tensor))] += 1
for output_tensor in tf.nest.flatten(node.outputs):
available_tensors.add(str(id(output_tensor)))
for tensor in self.outputs:
tensor_usage_count[str(id(tensor))] += 1
self._tensor_usage_count = tensor_usage_count
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725,076 | tf_keras.src.engine.functional | _conform_to_reference_input | Set shape and dtype based on `keras.Input`s. | def _conform_to_reference_input(self, tensor, ref_input):
"""Set shape and dtype based on `keras.Input`s."""
if isinstance(tensor, tf.Tensor):
# Allow (None,) and (None, 1) Tensors to be passed interchangeably.
# Use the shape specified by the `keras.Input`.
t_shape = tensor.shape
t_rank = t_shape.rank
ref_shape = ref_input.shape
ref_rank = ref_shape.rank
keras_history = getattr(tensor, "_keras_history", None)
if t_rank is not None and ref_rank is not None:
# Should squeeze last dimension. True if tensor is (BATCH, ...,
# 1) and reference is (BATCH, ...).
if t_rank == ref_rank + 1 and t_shape[-1] == 1:
tensor = tf.squeeze(tensor, axis=-1)
# Should expand last_dimension. True if tensor is (BATCH, ...)
# and reference is (BATCH, ..., 1).
elif t_rank == ref_rank - 1 and ref_shape[-1] == 1:
tensor = tf.expand_dims(tensor, axis=-1)
if keras_history is not None: # Restore keras history.
tensor._keras_history = keras_history
# Dtype casting.
tensor = tf.cast(tensor, dtype=ref_input.dtype)
elif tf_utils.is_extension_type(tensor):
# Dtype casting (If the extension type has a non-variant dtype and
# supports being cast). Only cast if necessary (since some
# extension types may not implement tf.cast).
tensor_dtype = getattr(tensor, "dtype", None)
ref_input_dtype = getattr(ref_input, "dtype", None)
if (
ref_input_dtype is not None
and tensor_dtype is not None
and tensor_dtype != ref_input_dtype
and ref_input_dtype != tf.variant
):
tensor = tf.cast(tensor, dtype=ref_input_dtype)
return tensor
| (self, tensor, ref_input) | [
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725,087 | tf_keras.src.engine.functional | _flatten_to_reference_inputs | Maps `tensors` to their respective `keras.Input`. | def _flatten_to_reference_inputs(self, tensors):
"""Maps `tensors` to their respective `keras.Input`."""
if self._enable_dict_to_input_mapping and isinstance(tensors, dict):
ref_inputs = self._nested_inputs
if not tf.nest.is_nested(ref_inputs):
ref_inputs = [self._nested_inputs]
if isinstance(ref_inputs, dict):
# In the case that the graph is constructed with dict input
# tensors, We will use the original dict key to map with the
# keys in the input data. Note that the model.inputs is using
# nest.flatten to process the input tensors, which means the
# dict input tensors are ordered by their keys.
ref_input_names = sorted(ref_inputs.keys())
else:
ref_input_names = [
inp._keras_history.layer.name for inp in ref_inputs
]
# Raise an warning if there are more input data comparing to input
# tensor
if len(tensors) > len(ref_input_names):
warnings.warn(
"Input dict contained keys {} which did not match any "
"model input. They will be ignored by the model.".format(
[n for n in tensors.keys() if n not in ref_input_names]
),
stacklevel=2,
)
try:
# Flatten in the order `Input`s were passed during Model
# construction.
return [tensors[n] for n in ref_input_names]
except KeyError:
# TODO(b/151582614)
return tf.nest.flatten(tensors)
# Otherwise both self.inputs and tensors will already be in same order.
return tf.nest.flatten(tensors)
| (self, tensors) | [
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725,098 | tf_keras.src.engine.functional | _get_save_spec | null | def _get_save_spec(self, dynamic_batch=True, inputs_only=True):
if getattr(self, "_has_explicit_input_shape", True):
# Functional models and Sequential models that have an explicit
# input shape should use the batch size set by the input layer.
dynamic_batch = False
return super()._get_save_spec(dynamic_batch, inputs_only)
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725,102 | tf_keras.src.engine.functional | _graph_network_add_loss | null | def _graph_network_add_loss(self, symbolic_loss):
new_nodes, new_layers = _map_subgraph_network(
self.inputs, [symbolic_loss]
)
# Losses must be keyed on inputs no matter what in order to be supported
# in DistributionStrategy.
add_loss_layer = base_layer.AddLoss(
unconditional=False, dtype=symbolic_loss.dtype
)
add_loss_layer(symbolic_loss)
new_nodes.extend(add_loss_layer.inbound_nodes)
new_layers.append(add_loss_layer)
self._insert_layers(new_layers, new_nodes)
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725,103 | tf_keras.src.engine.functional | _graph_network_add_metric | null | def _graph_network_add_metric(self, value, aggregation, name):
new_nodes, new_layers = _map_subgraph_network(self.inputs, [value])
add_metric_layer = base_layer.AddMetric(
aggregation, name, dtype=value.dtype
)
add_metric_layer(value)
new_nodes.extend(add_metric_layer.inbound_nodes)
new_layers.append(add_metric_layer)
self._insert_layers(new_layers, new_nodes)
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725,106 | tf_keras.src.engine.functional | _handle_deferred_layer_dependencies | Handles layer checkpoint dependencies that are added after init. | def _handle_deferred_layer_dependencies(self, layers):
"""Handles layer checkpoint dependencies that are added after init."""
layer_checkpoint_dependencies = self._layer_checkpoint_dependencies
layer_to_name = {v: k for k, v in layer_checkpoint_dependencies.items()}
for layer in layers:
if layer in layer_to_name:
self._handle_deferred_dependencies(
name=layer_to_name[layer], trackable=layer
)
| (self, layers) | [
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725,113 | tf_keras.src.engine.functional | _init_graph_network | @tf.__internal__.tracking.no_automatic_dependency_tracking
def _init_graph_network(self, inputs, outputs):
# This method is needed for Sequential to reinitialize graph network
# when layer is added or removed.
self._is_graph_network = True
# Normalize and set self.inputs, self.outputs.
if isinstance(inputs, list) and len(tf.nest.flatten(inputs)) == 1:
inputs = inputs[0]
if isinstance(outputs, list) and len(tf.nest.flatten(outputs)) == 1:
outputs = outputs[0]
self._nested_inputs = inputs
self._nested_outputs = outputs
self.inputs = tf.nest.flatten(inputs)
self.outputs = tf.nest.flatten(outputs)
# Models constructed with a single Tensor or list of Tensors can
# be called with a dict, where the keys of the dict are the names
# of the `Input` objects. Extra keys are ignored with warning.
if not tf.nest.is_nested(self._nested_inputs):
self._enable_dict_to_input_mapping = True
elif isinstance(self._nested_inputs, (list, tuple)) and not any(
tf.nest.is_nested(t) for t in self._nested_inputs
):
self._enable_dict_to_input_mapping = True
elif isinstance(self._nested_inputs, dict) and not any(
tf.nest.is_nested(t) for t in self._nested_inputs.values()
):
self._enable_dict_to_input_mapping = True
else:
self._enable_dict_to_input_mapping = False
if not tf.compat.v1.executing_eagerly_outside_functions():
if any(
not hasattr(tensor, "_keras_history") for tensor in self.outputs
):
base_layer_utils.create_keras_history(self._nested_outputs)
self._validate_graph_inputs_and_outputs()
# A Network does not create weights of its own, thus it is already
# built.
self.built = True
self._build_input_shape = tf.nest.map_structure(
lambda x: x.shape, inputs
)
self._compute_output_and_mask_jointly = True
# `_expects_training_arg` is True since the `training` argument is
# always present in the signature of the `call` method of a graph
# network.
self._call_spec.expects_training_arg = True
self._call_spec.expects_mask_arg = True
# A graph network does not autocast inputs, as its layers will cast them
# instead.
self._autocast = False
self._input_layers = []
self._output_layers = []
self._input_coordinates = []
self._output_coordinates = []
# This is for performance optimization when calling the Network on new
# inputs. Every time the Network is called on a set on input tensors, we
# compute the output tensors, output masks and output shapes in one
# pass, then cache them here. When any of these outputs is queried
# later, we retrieve it from there instead of recomputing it.
self._output_mask_cache = {}
self._output_tensor_cache = {}
self._output_shape_cache = {}
# Build self._output_layers:
for x in self.outputs:
(
layer,
node_index,
tensor_index,
) = x._keras_history
self._output_layers.append(layer)
self._output_coordinates.append((layer, node_index, tensor_index))
# Build self._input_layers:
for x in self.inputs:
(
layer,
node_index,
tensor_index,
) = x._keras_history
# It's supposed to be an input layer, so only one node
# and one tensor output.
assert node_index == 0
assert tensor_index == 0
self._input_layers.append(layer)
self._input_coordinates.append((layer, node_index, tensor_index))
# Keep track of the network's nodes and layers.
nodes, nodes_by_depth, layers, _ = _map_graph_network(
self.inputs, self.outputs
)
self._network_nodes = nodes
self._nodes_by_depth = nodes_by_depth
self._self_tracked_trackables = layers
self._layer_call_argspecs = {}
for layer in self._self_tracked_trackables:
self._layer_call_argspecs[layer] = tf_inspect.getfullargspec(
layer.call
)
# Build self.input_names and self.output_names.
self._set_output_names()
self.input_names = []
self._feed_input_names = []
self._feed_inputs = []
self._feed_input_shapes = []
for layer in self._input_layers:
self.input_names.append(layer.name)
if layer.is_placeholder:
self._feed_input_names.append(layer.name)
# Use batch_input_shape here because non-eager composite tensors
# may not have a shape attribute that's meaningful (sparse, for
# instance, has a tensor that's non-constant and needs to be
# fed). This means that input layers that create placeholders
# will need to have the batch_input_shape attr to allow for
# input shape validation.
self._feed_input_shapes.append(layer._batch_input_shape)
self._feed_inputs.append(layer.input)
self._compute_tensor_usage_count()
self._set_save_spec(self._nested_inputs)
tf_utils.assert_no_legacy_layers(self.layers)
# Note that this method is used by both functional and sequential
# models, so we can't just have this method in functional.__init__,
# which will miss the coverage of sequential model.
if self._layout_map is not None:
layout_map_lib._map_functional_model_variable(
self, self._layout_map
)
| (self, inputs, outputs) | [
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|
725,114 | tf_keras.src.engine.functional | _init_set_name | null | def _init_set_name(self, name, zero_based=True):
if not name:
cls_name = self.__class__.__name__
if self.__class__ == Functional:
# Hide the functional class name from user, since its not a
# public visible class. Use "Model" instead,
cls_name = "Model"
self._name = backend.unique_object_name(
generic_utils.to_snake_case(cls_name), zero_based=zero_based
)
else:
self._name = name
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] |
725,115 | tf_keras.src.engine.functional | _insert_layers | Inserts Layers into the Network after Network creation.
This is only valid for TF-Keras Graph Networks. Layers added via this
function will be included in the `call` computation and `get_config` of
this Network. They will not be added to the Network's outputs.
Args:
layers: Arbitrary nested structure of Layers. Layers must be reachable
from one or more of the `keras.Input` Tensors that correspond to
this Network's inputs.
relevant_nodes: Nodes from the Layers that should be considered part
of this Network. If `None`, all Nodes will be considered part of
this Network.
Raises:
ValueError: If the layers depend on `Input`s not found in this Model.
| def _insert_layers(self, layers, relevant_nodes=None):
"""Inserts Layers into the Network after Network creation.
This is only valid for TF-Keras Graph Networks. Layers added via this
function will be included in the `call` computation and `get_config` of
this Network. They will not be added to the Network's outputs.
Args:
layers: Arbitrary nested structure of Layers. Layers must be reachable
from one or more of the `keras.Input` Tensors that correspond to
this Network's inputs.
relevant_nodes: Nodes from the Layers that should be considered part
of this Network. If `None`, all Nodes will be considered part of
this Network.
Raises:
ValueError: If the layers depend on `Input`s not found in this Model.
"""
layers = tf.nest.flatten(layers)
tf_utils.assert_no_legacy_layers(layers)
node_to_depth = {}
for depth, nodes in self._nodes_by_depth.items():
node_to_depth.update({node: depth for node in nodes})
# The nodes of these Layers that are relevant to this Network. If not
# provided, assume all Nodes are relevant
if not relevant_nodes:
relevant_nodes = tf.nest.flatten(
[layer._inbound_nodes for layer in layers]
)
network_nodes = set(relevant_nodes + list(node_to_depth.keys()))
def _get_min_depth(node):
"""Gets the minimum depth at which node can be computed."""
min_depth = 0
for layer, node_id, _, _ in node.iterate_inbound():
inbound_node = layer._inbound_nodes[node_id]
if inbound_node in node_to_depth:
min_depth = min(min_depth, node_to_depth[inbound_node])
elif inbound_node not in network_nodes:
continue
else:
# Previous relevant nodes haven't been processed yet.
return None
# New node is one shallower than its shallowest input.
return min_depth - 1
# Insert nodes into `_nodes_by_depth` and other node attrs.
unprocessed_nodes = copy.copy(relevant_nodes)
i = 0
while unprocessed_nodes:
i += 1
# Do a sanity check. This can occur if `Input`s from outside this
# Model are being relied on.
if i > 10000:
raise ValueError(
"Layers could not be added due to missing dependencies."
)
node = unprocessed_nodes.pop(0)
depth = _get_min_depth(node)
if depth is None: # Defer until inbound nodes are processed.
unprocessed_nodes.append(node)
continue
node_key = _make_node_key(
node.layer.name, node.layer._inbound_nodes.index(node)
)
if node_key not in self._network_nodes:
node_to_depth[node] = depth
self._network_nodes.add(node_key)
self._nodes_by_depth[depth].append(node)
# Insert layers and update other layer attrs.
layer_set = set(self._self_tracked_trackables)
deferred_layers = []
for layer in layers:
if layer not in layer_set:
self._self_tracked_trackables.append(layer)
deferred_layers.append(layer)
self._layer_call_argspecs[layer] = tf_inspect.getfullargspec(
layer.call
)
layer_set.add(layer)
self._handle_deferred_layer_dependencies(deferred_layers)
self._compute_tensor_usage_count()
| (self, layers, relevant_nodes=None) | [
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725,118 | tf_keras.src.engine.sequential | _is_layer_name_unique | null | def _is_layer_name_unique(self, layer):
for ref_layer in self.layers:
if layer.name == ref_layer.name and ref_layer is not layer:
return False
return True
| (self, layer) | [
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725,120 | tf_keras.src.engine.functional | _lookup_dependency | null | def _lookup_dependency(self, name, cached_dependencies=None):
if cached_dependencies:
return cached_dependencies.get(name)
# Fall back to slow lookup (`layer_checkpoint_dependencies` does a
# thorough check of all layer to see if they contain weights.)
layer_dependencies = self._layer_checkpoint_dependencies
if name in layer_dependencies:
return layer_dependencies[name]
return super()._lookup_dependency(name)
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725,133 | tf_keras.src.engine.functional | _run_internal_graph | Computes output tensors for new inputs.
# Note:
- Can be run on non-Keras tensors.
Args:
inputs: Tensor or nested structure of Tensors.
training: Boolean learning phase.
mask: (Optional) Tensor or nested structure of Tensors.
Returns:
output_tensors
| def _run_internal_graph(self, inputs, training=None, mask=None):
"""Computes output tensors for new inputs.
# Note:
- Can be run on non-Keras tensors.
Args:
inputs: Tensor or nested structure of Tensors.
training: Boolean learning phase.
mask: (Optional) Tensor or nested structure of Tensors.
Returns:
output_tensors
"""
inputs = self._flatten_to_reference_inputs(inputs)
if mask is None:
masks = [None] * len(inputs)
else:
masks = self._flatten_to_reference_inputs(mask)
for input_t, mask in zip(inputs, masks):
input_t._keras_mask = mask
# Dictionary mapping reference tensors to computed tensors.
tensor_dict = {}
tensor_usage_count = self._tensor_usage_count
for x, y in zip(self.inputs, inputs):
y = self._conform_to_reference_input(y, ref_input=x)
x_id = str(id(x))
tensor_dict[x_id] = [y] * tensor_usage_count[x_id]
nodes_by_depth = self._nodes_by_depth
depth_keys = list(nodes_by_depth.keys())
depth_keys.sort(reverse=True)
for depth in depth_keys:
nodes = nodes_by_depth[depth]
for node in nodes:
if node.is_input:
continue # Input tensors already exist.
if any(t_id not in tensor_dict for t_id in node.flat_input_ids):
continue # Node is not computable, try skipping.
args, kwargs = node.map_arguments(tensor_dict)
outputs = node.layer(*args, **kwargs)
# Update tensor_dict.
for x_id, y in zip(
node.flat_output_ids, tf.nest.flatten(outputs)
):
tensor_dict[x_id] = [y] * tensor_usage_count[x_id]
output_tensors = []
for x in self.outputs:
x_id = str(id(x))
assert x_id in tensor_dict, "Could not compute output " + str(x)
output_tensors.append(tensor_dict[x_id].pop())
return tf.nest.pack_sequence_as(self._nested_outputs, output_tensors)
| (self, inputs, training=None, mask=None) | [
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725,141 | tf_keras.src.engine.functional | _set_output_names | Assigns unique names to the Network's outputs.
Output layers with multiple output tensors would otherwise lead to
duplicate names in self.output_names.
| def _set_output_names(self):
"""Assigns unique names to the Network's outputs.
Output layers with multiple output tensors would otherwise lead to
duplicate names in self.output_names.
"""
uniquified = []
output_names = set()
prefix_count = {}
for layer in self._output_layers:
proposal = layer.name
while proposal in output_names:
existing_count = prefix_count.get(layer.name, 1)
proposal = f"{layer.name}_{existing_count}"
prefix_count[layer.name] = existing_count + 1
output_names.add(proposal)
uniquified.append(proposal)
self.output_names = uniquified
| (self) | [
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725,151 | tf_keras.src.engine.functional | _trackable_children | null | def _trackable_children(self, save_type="checkpoint", **kwargs):
dependencies = self._layer_checkpoint_dependencies
dependencies.update(super()._trackable_children(save_type, **kwargs))
return dependencies
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725,156 | tf_keras.src.engine.functional | _validate_graph_inputs_and_outputs | Validates the inputs and outputs of a Graph Network. | def _validate_graph_inputs_and_outputs(self):
"""Validates the inputs and outputs of a Graph Network."""
# Check for redundancy in inputs.
if len({id(i) for i in self.inputs}) != len(self.inputs):
raise ValueError(
"The list of inputs passed to the model "
"contains the same input multiple times. "
"All inputs should only appear once."
f"Received inputs={self.inputs}"
)
for x in self.inputs:
# Check that x has appropriate `_keras_history` metadata.
if not hasattr(x, "_keras_history"):
cls_name = self.__class__.__name__
raise ValueError(
f"Input tensors to a {cls_name} model "
"must come from `tf.keras.Input`. "
f"Received inputs={x} (missing previous layer metadata)."
)
# Check that x is an input tensor.
layer = x._keras_history.layer
if len(layer._inbound_nodes) > 1 or (
layer._inbound_nodes and not layer._inbound_nodes[0].is_input
):
cls_name = self.__class__.__name__
logging.warning(
f"{cls_name} model inputs must come from "
"`tf.keras.Input` (thus holding past layer metadata). "
"They cannot be the output of "
"a previous non-Input layer. "
"Here, a tensor specified as "
f'input to "{self.name}" was not an Input tensor, '
f'it was generated by layer "{layer.name}".\n'
"Note that input tensors are "
"instantiated via `tensor = tf.keras.Input(shape)`.\n"
f"The tensor that caused the issue was: {x}"
)
# Check compatibility of batch sizes of Input Layers.
input_batch_sizes = set(
[
training_utils.get_static_batch_size(x._keras_history.layer)
for x in self.inputs
]
)
input_batch_sizes.discard(None)
if len(input_batch_sizes) > 1:
logging.warning(
"Found incompatible static batch sizes among the "
f"inputs. Batch sizes: {sorted(input_batch_sizes)}"
)
for x in self.outputs:
if not hasattr(x, "_keras_history"):
cls_name = self.__class__.__name__
raise ValueError(
f"Output tensors of a {cls_name} model must be "
"the output of a TensorFlow `Layer` "
f"(thus holding past layer metadata). Found: {x}"
)
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] |
725,158 | tf_keras.src.engine.sequential | add | Adds a layer instance on top of the layer stack.
Args:
layer: layer instance.
Raises:
TypeError: If `layer` is not a layer instance.
ValueError: In case the `layer` argument does not
know its input shape.
ValueError: In case the `layer` argument has
multiple output tensors, or is already connected
somewhere else (forbidden in `Sequential` models).
| @tf.__internal__.tracking.no_automatic_dependency_tracking
@traceback_utils.filter_traceback
def add(self, layer):
"""Adds a layer instance on top of the layer stack.
Args:
layer: layer instance.
Raises:
TypeError: If `layer` is not a layer instance.
ValueError: In case the `layer` argument does not
know its input shape.
ValueError: In case the `layer` argument has
multiple output tensors, or is already connected
somewhere else (forbidden in `Sequential` models).
"""
# If we are passed a TF-Keras tensor created by keras.Input(), we can
# extract the input layer from its keras history and use that without
# any loss of
# generality.
if hasattr(layer, "_keras_history"):
origin_layer = layer._keras_history[0]
if isinstance(origin_layer, input_layer.InputLayer):
layer = origin_layer
if isinstance(layer, tf.Module):
if not isinstance(layer, base_layer.Layer):
layer = functional.ModuleWrapper(layer)
else:
raise TypeError(
"The added layer must be an instance of class Layer. "
f"Received: layer={layer} of type {type(layer)}."
)
tf_utils.assert_no_legacy_layers([layer])
if not self._is_layer_name_unique(layer):
raise ValueError(
"All layers added to a Sequential model "
f'should have unique names. Name "{layer.name}" is already '
"the name of a layer in this model. Update the `name` argument "
"to pass a unique name."
)
self.built = False
set_inputs = False
self._maybe_create_attribute("_self_tracked_trackables", [])
if not self._self_tracked_trackables:
if isinstance(layer, input_layer.InputLayer):
# Case where the user passes an Input or InputLayer layer via
# `add`.
set_inputs = True
else:
batch_shape, dtype = training_utils.get_input_shape_and_dtype(
layer
)
if batch_shape:
# Instantiate an input layer.
x = input_layer.Input(
batch_shape=batch_shape,
dtype=dtype,
name=layer.name + "_input",
)
# This will build the current layer
# and create the node connecting the current layer
# to the input layer we just created.
layer(x)
set_inputs = True
if set_inputs:
outputs = tf.nest.flatten(layer._inbound_nodes[-1].outputs)
if len(outputs) != 1:
raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG)
self.outputs = outputs
self.inputs = layer_utils.get_source_inputs(self.outputs[0])
self.built = True
self._has_explicit_input_shape = True
elif self.outputs:
# If the model is being built continuously on top of an input layer:
# refresh its output.
output_tensor = layer(self.outputs[0])
if len(tf.nest.flatten(output_tensor)) != 1:
raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG)
self.outputs = [output_tensor]
self.built = True
if set_inputs or self._graph_initialized:
self._init_graph_network(self.inputs, self.outputs)
self._graph_initialized = True
else:
self._self_tracked_trackables.append(layer)
self._handle_deferred_layer_dependencies([layer])
self._layer_call_argspecs[layer] = tf_inspect.getfullargspec(layer.call)
| (self, layer) | [
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725,164 | tf_keras.src.engine.sequential | build | null | @generic_utils.default
def build(self, input_shape=None):
if self._graph_initialized:
self._init_graph_network(self.inputs, self.outputs)
else:
if input_shape is None:
raise ValueError("You must provide an `input_shape` argument.")
self._build_graph_network_for_inferred_shape(input_shape)
if not self.built:
input_shape = tuple(input_shape)
self._build_input_shape = input_shape
super().build(input_shape)
self.built = True
| (self, input_shape=None) | [
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725,166 | tf_keras.src.engine.sequential | call | null | def call(self, inputs, training=None, mask=None):
# If applicable, update the static input shape of the model.
if not self._has_explicit_input_shape:
if not tf.is_tensor(inputs) and not isinstance(inputs, tf.Tensor):
# This is a Sequential with multiple inputs. This is technically
# an invalid use case of Sequential, but we tolerate it for
# backwards compatibility.
self._use_legacy_deferred_behavior = True
self._build_input_shape = tf.nest.map_structure(
_get_shape_tuple, inputs
)
else:
self._build_graph_network_for_inferred_shape(
inputs.shape, inputs.dtype
)
if self._graph_initialized:
if not self.built:
self._init_graph_network(self.inputs, self.outputs)
return super().call(inputs, training=training, mask=mask)
outputs = inputs # handle the corner case where self.layers is empty
for layer in self.layers:
# During each iteration, `inputs` are the inputs to `layer`, and
# `outputs` are the outputs of `layer` applied to `inputs`. At the
# end of each iteration `inputs` is set to `outputs` to prepare for
# the next layer.
kwargs = {}
argspec = self._layer_call_argspecs[layer].args
if "mask" in argspec:
kwargs["mask"] = mask
if "training" in argspec:
kwargs["training"] = training
outputs = layer(inputs, **kwargs)
inputs = outputs
def _get_mask_from_keras_tensor(kt):
return getattr(kt, "_keras_mask", None)
mask = tf.nest.map_structure(_get_mask_from_keras_tensor, outputs)
return outputs
| (self, inputs, training=None, mask=None) | [
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725,170 | tf_keras.src.engine.sequential | compute_mask | null | def compute_mask(self, inputs, mask):
# TODO(omalleyt): b/123540974 This function is not really safe to call
# by itself because it will duplicate any updates and losses in graph
# mode by `call`ing the Layers again.
outputs = self.call(inputs, mask=mask)
return getattr(outputs, "_keras_mask", None)
| (self, inputs, mask) | [
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725,172 | tf_keras.src.engine.sequential | compute_output_shape | null | def compute_output_shape(self, input_shape):
shape = input_shape
for layer in self.layers:
shape = layer.compute_output_shape(shape)
return shape
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725,183 | tf_keras.src.engine.sequential | get_config | null | def get_config(self):
layer_configs = []
serialize_obj_fn = serialization_lib.serialize_keras_object
if getattr(self, "use_legacy_config", None):
serialize_obj_fn = legacy_serialization.serialize_keras_object
for layer in super().layers:
# `super().layers` include the InputLayer if available (it is
# filtered out of `self.layers`). Note that
# `self._self_tracked_trackables` is managed by the tracking
# infrastructure and should not be used.
layer_configs.append(serialize_obj_fn(layer))
config = training.Model.get_config(self)
config["name"] = self.name
config["layers"] = copy.deepcopy(layer_configs)
if not self._is_graph_network and self._build_input_shape is not None:
config["build_input_shape"] = self._build_input_shape
return config
| (self) | [
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725,192 | tf_keras.src.engine.functional | get_weight_paths | null | def get_weight_paths(self):
result = {}
for layer in self.layers:
(
descendants,
object_paths_dict,
) = tf.__internal__.tracking.ObjectGraphView(
layer
).breadth_first_traversal()
for descendant in descendants:
if isinstance(descendant, tf.Variable):
trackable_references = object_paths_dict[descendant]
object_path = ".".join(
[t.name for t in trackable_references]
)
result[layer.name + "." + object_path] = descendant
return result
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] |
725,199 | tf_keras.src.engine.sequential | pop | Removes the last layer in the model.
Raises:
TypeError: if there are no layers in the model.
| @tf.__internal__.tracking.no_automatic_dependency_tracking
@traceback_utils.filter_traceback
def add(self, layer):
"""Adds a layer instance on top of the layer stack.
Args:
layer: layer instance.
Raises:
TypeError: If `layer` is not a layer instance.
ValueError: In case the `layer` argument does not
know its input shape.
ValueError: In case the `layer` argument has
multiple output tensors, or is already connected
somewhere else (forbidden in `Sequential` models).
"""
# If we are passed a TF-Keras tensor created by keras.Input(), we can
# extract the input layer from its keras history and use that without
# any loss of
# generality.
if hasattr(layer, "_keras_history"):
origin_layer = layer._keras_history[0]
if isinstance(origin_layer, input_layer.InputLayer):
layer = origin_layer
if isinstance(layer, tf.Module):
if not isinstance(layer, base_layer.Layer):
layer = functional.ModuleWrapper(layer)
else:
raise TypeError(
"The added layer must be an instance of class Layer. "
f"Received: layer={layer} of type {type(layer)}."
)
tf_utils.assert_no_legacy_layers([layer])
if not self._is_layer_name_unique(layer):
raise ValueError(
"All layers added to a Sequential model "
f'should have unique names. Name "{layer.name}" is already '
"the name of a layer in this model. Update the `name` argument "
"to pass a unique name."
)
self.built = False
set_inputs = False
self._maybe_create_attribute("_self_tracked_trackables", [])
if not self._self_tracked_trackables:
if isinstance(layer, input_layer.InputLayer):
# Case where the user passes an Input or InputLayer layer via
# `add`.
set_inputs = True
else:
batch_shape, dtype = training_utils.get_input_shape_and_dtype(
layer
)
if batch_shape:
# Instantiate an input layer.
x = input_layer.Input(
batch_shape=batch_shape,
dtype=dtype,
name=layer.name + "_input",
)
# This will build the current layer
# and create the node connecting the current layer
# to the input layer we just created.
layer(x)
set_inputs = True
if set_inputs:
outputs = tf.nest.flatten(layer._inbound_nodes[-1].outputs)
if len(outputs) != 1:
raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG)
self.outputs = outputs
self.inputs = layer_utils.get_source_inputs(self.outputs[0])
self.built = True
self._has_explicit_input_shape = True
elif self.outputs:
# If the model is being built continuously on top of an input layer:
# refresh its output.
output_tensor = layer(self.outputs[0])
if len(tf.nest.flatten(output_tensor)) != 1:
raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG)
self.outputs = [output_tensor]
self.built = True
if set_inputs or self._graph_initialized:
self._init_graph_network(self.inputs, self.outputs)
self._graph_initialized = True
else:
self._self_tracked_trackables.append(layer)
self._handle_deferred_layer_dependencies([layer])
self._layer_call_argspecs[layer] = tf_inspect.getfullargspec(layer.call)
| (self) | [
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725,242 | safetensors_rust | SafetensorError | Custom Python Exception for Safetensor errors. | from builtins import type
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725,244 | builtins | safe_open | Opens a safetensors lazily and returns tensors as asked
Args:
filename (`str`, or `os.PathLike`):
The filename to open
framework (`str`):
The framework you want you tensors in. Supported values:
`pt`, `tf`, `flax`, `numpy`.
device (`str`, defaults to `"cpu"`):
The device on which you want the tensors. | from builtins import safe_open
| (self, filename, framework, device='cpu') | [
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725,247 | notebook | _jupyter_labextension_paths | null | def _jupyter_labextension_paths() -> list[dict[str, str]]:
return [{"src": "labextension", "dest": "@jupyter-notebook/lab-extension"}]
| () -> list[dict[str, str]] | [
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725,248 | notebook | _jupyter_server_extension_paths | null | def _jupyter_server_extension_paths() -> list[dict[str, str]]:
return [{"module": "notebook"}]
| () -> list[dict[str, str]] | [
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725,249 | notebook | _jupyter_server_extension_points | null | def _jupyter_server_extension_points() -> list[dict[str, Any]]:
from .app import JupyterNotebookApp
return [{"module": "notebook", "app": JupyterNotebookApp}]
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725,251 | dockerfile_parse.parser | DockerfileParser | null | class DockerfileParser(object):
def __init__(self, path=None,
cache_content=False,
env_replace=True,
parent_env=None,
fileobj=None,
build_args=None):
"""
Initialize source of Dockerfile
:param path: path to (directory with) Dockerfile; if not provided,
and fileobj is not provided, the current working
directory will be used
:param cache_content: cache Dockerfile content inside DockerfileParser
:param env_replace: return content with variables replaced
:param parent_env: python dict of inherited env vars from parent image
:param fileobj: seekable file-like object containing Dockerfile content
as bytes (will be truncated on write)
:param build_args: python dict of build args used when building image
"""
self.fileobj = fileobj
if self.fileobj is not None:
if path is not None:
raise ValueError("Parameters path and fileobj cannot be used together.")
else:
self.fileobj.seek(0)
else:
path = path or '.'
if path.endswith(DOCKERFILE_FILENAME):
self.dockerfile_path = path
else:
self.dockerfile_path = os.path.join(path, DOCKERFILE_FILENAME)
self.cache_content = cache_content
self.cached_content = '' # unicode string
if cache_content:
try:
# this will cache the Dockerfile content
self.content
except (IOError, OSError):
# the Dockerfile doesn't exist yet
pass
self.env_replace = env_replace
if parent_env is None:
self.parent_env = {}
else:
assert isinstance(parent_env, dict)
logger.debug("Setting inherited parent image ENV vars: %s", parent_env)
self.parent_env = parent_env
if build_args is None:
self.build_args = {}
else:
assert isinstance(build_args, dict)
logger.debug("Setting build args: %s", build_args)
self.build_args = build_args
@contextmanager
def _open_dockerfile(self, mode):
if self.fileobj is not None:
self.fileobj.seek(0)
if 'w' in mode:
self.fileobj.truncate()
yield self.fileobj
self.fileobj.seek(0)
else:
with open(self.dockerfile_path, mode) as dockerfile:
yield dockerfile
@property
def lines(self):
"""
:return: list containing lines (unicode) from Dockerfile
"""
if self.cache_content and self.cached_content:
return self.cached_content.splitlines(True)
try:
with self._open_dockerfile('rb') as dockerfile:
lines = [b2u(line) for line in dockerfile.readlines()]
if self.cache_content:
self.cached_content = ''.join(lines)
return lines
except (IOError, OSError) as ex:
logger.error("Couldn't retrieve lines from dockerfile: %r", ex)
raise
@lines.setter
def lines(self, lines):
"""
Fill Dockerfile content with specified lines
:param lines: list of lines to be written to Dockerfile
"""
if self.cache_content:
self.cached_content = ''.join(b2u(line) for line in lines)
try:
with self._open_dockerfile('wb') as dockerfile:
dockerfile.writelines(u2b(line) for line in lines)
except (IOError, OSError) as ex:
logger.error("Couldn't write lines to dockerfile: %r", ex)
raise
@property
def content(self):
"""
:return: string (unicode) with Dockerfile content
"""
if self.cache_content and self.cached_content:
return self.cached_content
try:
with self._open_dockerfile('rb') as dockerfile:
content = b2u(dockerfile.read())
if self.cache_content:
self.cached_content = content
return content
except (IOError, OSError) as ex:
logger.error("Couldn't retrieve content of dockerfile: %r", ex)
raise
@content.setter
def content(self, content):
"""
Overwrite Dockerfile with specified content
:param content: string to be written to Dockerfile
"""
if self.cache_content:
self.cached_content = b2u(content)
try:
with self._open_dockerfile('wb') as dockerfile:
dockerfile.write(u2b(content))
except (IOError, OSError) as ex:
logger.error("Couldn't write content to dockerfile: %r", ex)
raise
@property
def structure(self):
"""
Returns a list of dicts describing the commands:
[
{"instruction": "FROM", # always upper-case
"startline": 0, # 0-based
"endline": 0, # 0-based
"content": "From fedora\n",
"value": "fedora"},
{"instruction": "CMD",
"startline": 1,
"endline": 2,
"content": "CMD yum -y update && \\\n yum clean all\n",
"value": "yum -y update && yum clean all"}
]
"""
def _rstrip_eol(text, line_continuation_char='\\'):
text = text.rstrip()
if text.endswith(line_continuation_char):
return text[:-1]
return text
def _create_instruction_dict(instruction=None, value=None):
return {
'instruction': instruction,
'startline': lineno,
'endline': lineno,
'content': line, # pylint: disable=undefined-loop-variable
'value': value
}
def _clean_comment_line(line):
line = re.sub(r'^\s*#\s*', '', line)
line = re.sub(r'\n', '', line)
return line
instructions = []
lineno = -1
line_continuation_char = '\\'
insnre = re.compile(r'^\s*(\S+)\s+(.*)$') # matched group is insn
contre = re.compile(r'^.*\\\s*$') # line continues?
commentre = re.compile(r'^\s*#') # line is a comment?
directive_possible = True
# escape directive regex
escape_directive_re = re.compile(r'^\s*#\s*escape\s*=\s*(\\|`)\s*$', re.I)
# syntax directive regex
syntax_directive_re = re.compile(r'^\s*#\s*syntax\s*=\s*(.*)\s*$', re.I)
in_continuation = False
current_instruction = {}
for line in self.lines:
lineno += 1
if directive_possible:
# once support for python versions before 3.8 is dropped use walrus operator
if escape_directive_re.match(line):
# Do the matching twice if there is a directive to avoid doing the matching
# for other lines
match = escape_directive_re.match(line)
line_continuation_char = match.group(1)
contre = re.compile(r'^.*' + re.escape(match.group(1)) + r'\s*$')
elif syntax_directive_re.match(line):
# Currently no information for the syntax directive is stored it is still
# necessary to detect escape directives after a syntax directive
pass
else:
directive_possible = False
# It is necessary to keep instructions and comment parsing separate,
# as a multi-line instruction can be interjected with comments.
if commentre.match(line):
comment = _create_instruction_dict(
instruction=COMMENT_INSTRUCTION,
value=_clean_comment_line(line)
)
instructions.ap | (path=None, cache_content=False, env_replace=True, parent_env=None, fileobj=None, build_args=None) | [
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725,252 | dockerfile_parse.parser | __init__ |
Initialize source of Dockerfile
:param path: path to (directory with) Dockerfile; if not provided,
and fileobj is not provided, the current working
directory will be used
:param cache_content: cache Dockerfile content inside DockerfileParser
:param env_replace: return content with variables replaced
:param parent_env: python dict of inherited env vars from parent image
:param fileobj: seekable file-like object containing Dockerfile content
as bytes (will be truncated on write)
:param build_args: python dict of build args used when building image
| def __init__(self, path=None,
cache_content=False,
env_replace=True,
parent_env=None,
fileobj=None,
build_args=None):
"""
Initialize source of Dockerfile
:param path: path to (directory with) Dockerfile; if not provided,
and fileobj is not provided, the current working
directory will be used
:param cache_content: cache Dockerfile content inside DockerfileParser
:param env_replace: return content with variables replaced
:param parent_env: python dict of inherited env vars from parent image
:param fileobj: seekable file-like object containing Dockerfile content
as bytes (will be truncated on write)
:param build_args: python dict of build args used when building image
"""
self.fileobj = fileobj
if self.fileobj is not None:
if path is not None:
raise ValueError("Parameters path and fileobj cannot be used together.")
else:
self.fileobj.seek(0)
else:
path = path or '.'
if path.endswith(DOCKERFILE_FILENAME):
self.dockerfile_path = path
else:
self.dockerfile_path = os.path.join(path, DOCKERFILE_FILENAME)
self.cache_content = cache_content
self.cached_content = '' # unicode string
if cache_content:
try:
# this will cache the Dockerfile content
self.content
except (IOError, OSError):
# the Dockerfile doesn't exist yet
pass
self.env_replace = env_replace
if parent_env is None:
self.parent_env = {}
else:
assert isinstance(parent_env, dict)
logger.debug("Setting inherited parent image ENV vars: %s", parent_env)
self.parent_env = parent_env
if build_args is None:
self.build_args = {}
else:
assert isinstance(build_args, dict)
logger.debug("Setting build args: %s", build_args)
self.build_args = build_args
| (self, path=None, cache_content=False, env_replace=True, parent_env=None, fileobj=None, build_args=None) | [
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725,253 | dockerfile_parse.parser | _add_instruction |
:param instruction: instruction name to be added
:param value: instruction value
| def _add_instruction(self, instruction, value):
"""
:param instruction: instruction name to be added
:param value: instruction value
"""
if instruction in ('LABEL', 'ENV', 'ARG') and len(value) == 2:
new_line = instruction + ' ' + '='.join(map(quote, value)) + '\n'
else:
new_line = '{0} {1}\n'.format(instruction, value)
if new_line:
lines = self.lines
if not lines[len(lines) - 1].endswith('\n'):
new_line = '\n' + new_line
lines += new_line
self.lines = lines
| (self, instruction, value) | [
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] |
725,254 | dockerfile_parse.parser | _delete_instructions |
:param instruction: name of instruction to be deleted
:param value: if specified, delete instruction only when it has this value
if instruction is LABEL then value is label name
| def _delete_instructions(self, instruction, value=None):
"""
:param instruction: name of instruction to be deleted
:param value: if specified, delete instruction only when it has this value
if instruction is LABEL then value is label name
"""
if instruction == 'LABEL' and value:
self._modify_instruction_label(value, None)
return
if instruction == 'ENV' and value:
self._modify_instruction_env(value, None)
return
if instruction == 'ARG' and value:
self._modify_instruction_arg(value, None)
return
lines = self.lines
deleted = False
for insn in reversed(self.structure):
if insn['instruction'] == instruction:
if value and insn['value'] != value:
continue
deleted = True
del lines[insn['startline']:insn['endline'] + 1]
if deleted:
self.lines = lines
| (self, instruction, value=None) | [
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] |
725,255 | dockerfile_parse.parser | _instruction_getter |
Get LABEL or ENV or ARG instructions with environment replacement
:param name: e.g. 'LABEL' or 'ENV' or 'ARG'
:param env_replace: bool, whether to perform ENV substitution
:return: Labels instance or Envs instance
| def _instruction_getter(self, name, env_replace):
"""
Get LABEL or ENV or ARG instructions with environment replacement
:param name: e.g. 'LABEL' or 'ENV' or 'ARG'
:param env_replace: bool, whether to perform ENV substitution
:return: Labels instance or Envs instance
"""
if name not in ('LABEL', 'ENV', 'ARG'):
raise ValueError("Unsupported instruction '{0}'".format(name))
in_stage = False
top_args = {}
instructions = {}
args = {}
envs = {}
for instruction_desc in self.structure:
this_instruction = instruction_desc['instruction']
if this_instruction == 'FROM':
in_stage = True
instructions.clear()
args = {}
envs = self.parent_env.copy()
elif this_instruction in (name, 'ENV', 'ARG'):
logger.debug("%s value: %r", name.lower(), instruction_desc['value'])
key_val_list = extract_key_values(
env_replace=this_instruction != 'ARG' and env_replace,
args=args, envs=envs,
instruction_value=instruction_desc['value'])
for key, value in key_val_list:
if this_instruction == 'ARG':
if in_stage:
if key in top_args:
value = top_args[key]
elif key in self.build_args:
value = self.build_args[key]
args[key] = value
else:
if key in self.build_args:
value = self.build_args[key]
top_args[key] = value
if this_instruction == name:
instructions[key] = value
logger.debug("new %s %r=%r", name.lower(), key, value)
if this_instruction == 'ENV':
envs[key] = value
logger.debug("instructions: %r", instructions)
if name == 'LABEL':
return Labels(instructions, self)
elif name == 'ENV':
return Envs(instructions, self)
else:
return Args(instructions, self)
| (self, name, env_replace) | [
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725,256 | dockerfile_parse.parser | _instructions_setter | null | def _instructions_setter(self, name, instructions):
if not isinstance(instructions, dict):
raise TypeError('instructions needs to be a dictionary {name: value}')
if name == 'LABEL':
existing = self.labels
elif name == 'ENV':
existing = self.envs
elif name == 'ARG':
existing = self.args
else:
raise ValueError("Unexpected instruction '%s'" % name)
logger.debug("setting %s instructions: %r", name, instructions)
to_delete = [k for k in existing if k not in instructions]
for key in to_delete:
logger.debug("delete %r", key)
self._modify_instruction_label_env(name, key, None)
to_add = dict((k, v) for (k, v) in instructions.items() if k not in existing)
for k, v in to_add.items():
logger.debug("add %r", k)
self._add_instruction(name, (k, v))
to_change = dict((k, v) for (k, v) in instructions.items()
if (k in existing and v != existing[k]))
for k, v in to_change.items():
logger.debug("modify %r", k)
self._modify_instruction_label_env(name, k, v)
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725,257 | dockerfile_parse.parser | _modify_instruction_arg | null | def _modify_instruction_arg(self, arg_key, arg_value):
self._modify_instruction_label_env('ARG', arg_key, arg_value)
| (self, arg_key, arg_value) | [
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725,258 | dockerfile_parse.parser | _modify_instruction_env | null | def _modify_instruction_env(self, env_var_key, env_var_value):
self._modify_instruction_label_env('ENV', env_var_key, env_var_value)
| (self, env_var_key, env_var_value) | [
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725,259 | dockerfile_parse.parser | _modify_instruction_label | null | def _modify_instruction_label(self, label_key, instr_value):
self._modify_instruction_label_env('LABEL', label_key, instr_value)
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725,260 | dockerfile_parse.parser | _modify_instruction_label_env |
set <INSTRUCTION> instr_key to instr_value
:param instr_key: str, label key
:param instr_value: str or None, new label/env value or None to remove
| def _modify_instruction_label_env(self, instruction, instr_key, instr_value):
"""
set <INSTRUCTION> instr_key to instr_value
:param instr_key: str, label key
:param instr_value: str or None, new label/env value or None to remove
"""
if instruction == 'LABEL':
instructions = self.labels
elif instruction == 'ENV':
instructions = self.envs
elif instruction == 'ARG':
instructions = self.args
else:
raise ValueError("Unknown instruction '%s'" % instruction)
if instr_key not in instructions:
raise KeyError('%s not in %ss' % (instr_key, instruction))
# extract target instructions from the final stage only
candidates = []
for insn in self.structure:
if insn['instruction'] == 'FROM':
candidates = []
if insn['instruction'] == instruction:
candidates.append(insn)
# Find where in the file to put the changes
content = startline = endline = None
for candidate in candidates:
words = list(WordSplitter(candidate['value']).split(dequote=False))
# LABEL/ENV/ARG syntax is one of two types:
if '=' not in words[0]: # LABEL/ENV/ARG name value
# Remove quotes from key name and see if it's the one
# we're looking for.
if WordSplitter(words[0]).dequote() == instr_key:
if instr_value is None:
# Delete this line altogether
content = None
else:
# Adjust label/env value
words[1:] = [quote(instr_value)]
# Now reconstruct the line
content = " ".join([instruction] + words) + '\n'
startline = candidate['startline']
endline = candidate['endline']
break
else: # LABEL/ENV/ARG "name"="value"
for index, token in enumerate(words):
key, _ = token.split("=", 1)
if WordSplitter(key).dequote() == instr_key:
if instr_value is None:
# Delete this label
del words[index]
else:
# Adjust label/env value
words[index] = "{0}={1}".format(key,
quote(instr_value))
if len(words) == 0:
# We removed the last label/env, delete the whole line
content = None
else:
# Now reconstruct the line
content = " ".join([instruction] + words) + '\n'
startline = candidate['startline']
endline = candidate['endline']
break
# We know the label/env we're looking for is there
assert startline and endline
# Re-write the Dockerfile
lines = self.lines
del lines[startline:endline + 1]
if content:
lines.insert(startline, content)
self.lines = lines
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] |
725,261 | dockerfile_parse.parser | _open_dockerfile | null | @property
def structure(self):
"""
Returns a list of dicts describing the commands:
[
{"instruction": "FROM", # always upper-case
"startline": 0, # 0-based
"endline": 0, # 0-based
"content": "From fedora\n",
"value": "fedora"},
{"instruction": "CMD",
"startline": 1,
"endline": 2,
"content": "CMD yum -y update && \\\n yum clean all\n",
"value": "yum -y update && yum clean all"}
]
"""
def _rstrip_eol(text, line_continuation_char='\\'):
text = text.rstrip()
if text.endswith(line_continuation_char):
return text[:-1]
return text
def _create_instruction_dict(instruction=None, value=None):
return {
'instruction': instruction,
'startline': lineno,
'endline': lineno,
'content': line, # pylint: disable=undefined-loop-variable
'value': value
}
def _clean_comment_line(line):
line = re.sub(r'^\s*#\s*', '', line)
line = re.sub(r'\n', '', line)
return line
instructions = []
lineno = -1
line_continuation_char = '\\'
insnre = re.compile(r'^\s*(\S+)\s+(.*)$') # matched group is insn
contre = re.compile(r'^.*\\\s*$') # line continues?
commentre = re.compile(r'^\s*#') # line is a comment?
directive_possible = True
# escape directive regex
escape_directive_re = re.compile(r'^\s*#\s*escape\s*=\s*(\\|`)\s*$', re.I)
# syntax directive regex
syntax_directive_re = re.compile(r'^\s*#\s*syntax\s*=\s*(.*)\s*$', re.I)
in_continuation = False
current_instruction = {}
for line in self.lines:
lineno += 1
if directive_possible:
# once support for python versions before 3.8 is dropped use walrus operator
if escape_directive_re.match(line):
# Do the matching twice if there is a directive to avoid doing the matching
# for other lines
match = escape_directive_re.match(line)
line_continuation_char = match.group(1)
contre = re.compile(r'^.*' + re.escape(match.group(1)) + r'\s*$')
elif syntax_directive_re.match(line):
# Currently no information for the syntax directive is stored it is still
# necessary to detect escape directives after a syntax directive
pass
else:
directive_possible = False
# It is necessary to keep instructions and comment parsing separate,
# as a multi-line instruction can be interjected with comments.
if commentre.match(line):
comment = _create_instruction_dict(
instruction=COMMENT_INSTRUCTION,
value=_clean_comment_line(line)
)
instructions.append(comment)
else:
if not in_continuation:
m = insnre.match(line)
if not m:
continue
current_instruction = _create_instruction_dict(
instruction=m.groups()[0].upper(),
value=_rstrip_eol(m.groups()[1], line_continuation_char)
)
else:
current_instruction['content'] += line
current_instruction['endline'] = lineno
if current_instruction['value']:
current_instruction['value'] += _rstrip_eol(line, line_continuation_char)
else:
current_instruction['value'] = _rstrip_eol(line.lstrip(),
line_continuation_char)
in_continuation = contre.match(line)
if not in_continuation and current_instruction:
instructions.append(current_instruction)
return instructions
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725,262 | dockerfile_parse.parser | add_lines |
Add lines to the beginning or end of the build.
:param lines: one or more lines to add to the content, by default at the end.
:param all_stages: bool for whether to add in all stages for a multistage build
or (by default) only the last.
:param at_start: adds at the beginning (after FROM) of the stage(s) instead of the end.
:param skip_scratch: skip stages which use "FROM scratch"
| def add_lines(self, *lines, **kwargs):
"""
Add lines to the beginning or end of the build.
:param lines: one or more lines to add to the content, by default at the end.
:param all_stages: bool for whether to add in all stages for a multistage build
or (by default) only the last.
:param at_start: adds at the beginning (after FROM) of the stage(s) instead of the end.
:param skip_scratch: skip stages which use "FROM scratch"
"""
assert len(lines) > 0
lines = [_endline(line) for line in lines]
all_stages = kwargs.pop('all_stages', False)
at_start = kwargs.pop('at_start', False)
skip_scratch = kwargs.pop('skip_scratch', False)
assert not kwargs, "Unknown keyword argument(s): {0}".format(list(kwargs))
froms = [
instr for instr in self.structure
if instr['instruction'] == 'FROM'
] or [{'endline': -1}] # no FROM? fake one before the beginning
if not all_stages: # only modify the last
froms = [froms[-1]]
df_lines = self.lines
# make sure last line has a newline if lines are to be appended
if df_lines and not at_start:
df_lines[-1] = _endline(df_lines[-1])
# iterate through the stages in reverse order
# so adding lines doesn't invalidate line numbers from structure dicts.
# first add a bogus instruction to represent EOF in our iteration.
froms.append({'startline': len(df_lines) + 1})
for stage in range(len(froms)-2, -1, -1): # e.g. 0 for single or 2, 1, 0 for 3 stages
start, finish = froms[stage], froms[stage+1]
linenum = start['endline'] + 1 if at_start else finish['startline']
image, _ = image_from(froms[stage].get('value') or '')
if skip_scratch and image == 'scratch':
continue
df_lines[linenum:linenum] = lines
self.lines = df_lines
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725,263 | dockerfile_parse.parser | add_lines_at |
Add lines at a specific location in the file.
:param anchor: structure_dict|line_str|line_num a reference to where adds should occur
:param lines: one or more lines to add to the content
:param replace: if True -- replace the anchor
:param after: if True -- insert after the anchor (conflicts with "replace")
| def add_lines_at(self, anchor, *lines, **kwargs):
"""
Add lines at a specific location in the file.
:param anchor: structure_dict|line_str|line_num a reference to where adds should occur
:param lines: one or more lines to add to the content
:param replace: if True -- replace the anchor
:param after: if True -- insert after the anchor (conflicts with "replace")
"""
assert len(lines) > 0
replace = kwargs.pop('replace', False)
after = kwargs.pop('after', False)
assert not (after and replace)
assert not kwargs, "Unknown keyword argument(s): {0}".format(list(kwargs))
# find the line number for the insertion
df_lines = self.lines
if isinstance(anchor, int): # line number, just validate
assert 0 <= anchor < len(df_lines)
if replace:
del df_lines[anchor]
elif isinstance(anchor, dict): # structure
assert anchor in self.structure, "Current structure does not match: {0}".format(anchor)
if replace:
df_lines[anchor['startline']:anchor['endline'] + 1] = []
if after:
anchor = anchor['endline']
else:
anchor = anchor['startline']
elif isinstance(anchor, str): # line contents
matches = [index for index, text in enumerate(df_lines) if text == anchor]
if not matches:
raise RuntimeError("Cannot find line in the build file:\n" + anchor)
anchor = matches[-1]
if replace:
del df_lines[anchor]
else:
raise RuntimeError("Unknown anchor type {0}".format(anchor))
if after:
# ensure there's a newline on final line
df_lines[anchor] = _endline(df_lines[anchor])
anchor += 1
df_lines[anchor:anchor] = [_endline(line) for line in lines]
self.lines = df_lines
| (self, anchor, *lines, **kwargs) | [
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725,378 | tushare.pro.data_pro | ht_subs | null | def ht_subs(username, password):
from tushare.subs.ht_subs.subscribe import InsightSubscribe
app = InsightSubscribe(username, password)
return app
| (username, password) | [
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] |
725,404 | tushare.util.verify_token | wrapper |
沪深京 A 股 all -实时行情
@param src: 数据源 新浪sina |东方财富 dc
@param interval: 分页采集时间间隔（默认3秒翻译一夜）
@param page_count: 限制抓取的页数（仅对新浪有效）
@param proxies: 设置代理 防止被封禁
@return: 按涨跌幅 倒序排序
-------
DataFrame 实时交易数据
东方财富：
2、代码:TS_CODE
3、名称:NAME
4、最新价:PRICE
5、涨跌幅:PCT_CHANGE
6、涨跌额:CHANGE
7、成交量:VOLUME
8、成交额:AMOUNT
9、振幅:SWING
10、最高:HIGH
11、最低:LOW
12、今开:OPEN
13、昨收:CLOSE
14、量比:VOL_RATIO
15、换手率:TURNOVER_RATE
16、市盈率-动态:PE
17、市净率:PB
18、总市值:TOTAL_MV
19、流通市值:FLOAT_MV
20、涨速:RISE
21、5分钟涨跌:5MIN
22、60日涨跌幅:60DAY
23、年初至今涨跌幅:1YEAR
新浪财经：
1、代码:TS_CODE
2、名称:NAME
3、最新价:PRICE
4、涨跌额:CHANGE
5、涨跌幅:PCT_CHANGE
6、买入:BUY
7、卖出:SALE
8、昨收:CLOSE
9、今开:OPEN
10、最高:HIGH
11、最低:LOW
12、成交量:VOLUME
13、成交额:AMOUNT
14、时间戳:TIME
| def require_permission(event_name, event_detail):
def decorator(func):
def wrapper(*args, **kwargs):
# event_name = kwargs["event_name"]
# event_detail = kwargs["event_detail"]
# 检查用户权限
token = get_token()
if token:
try:
r = verify_token(token, event_name, event_detail).json()
except Exception as err:
raise PermissionError(f"验证token出错，{err}")
if r.get("message") == "success":
# 如果有足够权限，调用原始函数
return func(*args, **kwargs)
else:
print(f"验证token出错，{r.text}")
raise PermissionError(f"{r['msg']}")
else:
raise PermissionError(ct.TOKEN_ERR_MSG)
wrapper.__doc__ = func.__doc__
return wrapper
return decorator
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725,405 | tushare.util.verify_token | wrapper |
获取实时交易数据 getting real time quotes data
用于跟踪交易情况（本次执行的结果-上一次执行的数据）
Parameters
------
ts_code : string
src : sina ，dc
return
-------
DataFrame 实时交易数据
属性:0：name，股票名字
1：open，今日开盘价
2：pre_close，昨日收盘价
3：price，当前价格
4：high，今日最高价
5：low，今日最低价
6：bid，竞买价，即“买一”报价
7：ask，竞卖价，即“卖一”报价
8：volumn，成交量 maybe you need do volumn/100
9：amount，成交金额（元 CNY）
10：b1_v，委买一（笔数 bid volume）
11：b1_p，委买一（价格 bid price）
12：b2_v，“买二”
13：b2_p，“买二”
14：b3_v，“买三”
15：b3_p，“买三”
16：b4_v，“买四”
17：b4_p，“买四”
18：b5_v，“买五”
19：b5_p，“买五”
20：a1_v，委卖一（笔数 ask volume）
21：a1_p，委卖一（价格 ask price）
...
30：date，日期；
31：time，时间；
| def require_permission(event_name, event_detail):
def decorator(func):
def wrapper(*args, **kwargs):
# event_name = kwargs["event_name"]
# event_detail = kwargs["event_detail"]
# 检查用户权限
token = get_token()
if token:
try:
r = verify_token(token, event_name, event_detail).json()
except Exception as err:
raise PermissionError(f"验证token出错，{err}")
if r.get("message") == "success":
# 如果有足够权限，调用原始函数
return func(*args, **kwargs)
else:
print(f"验证token出错，{r.text}")
raise PermissionError(f"{r['msg']}")
else:
raise PermissionError(ct.TOKEN_ERR_MSG)
wrapper.__doc__ = func.__doc__
return wrapper
return decorator
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725,406 | tushare.util.verify_token | wrapper |
历史分笔数据
:param ts_code: 股票代码
:type ts_code: str
:param src: 来源 腾讯财经tx 新浪财经sina
:type src: str
:param page_count: 限制页数
:type page_count: str
:return: 历史分笔数据
:rtype: pandas.DataFrame
1、TIME : 成交时间
2、PRICE : 成交价格
3、PCHANGE : 涨跌幅
4、CHANGE : 价格变动
5、VOLUME : 成交量(手)
6、AMOUNT : 成交额(元)
7、TYPE : 性质
| def require_permission(event_name, event_detail):
def decorator(func):
def wrapper(*args, **kwargs):
# event_name = kwargs["event_name"]
# event_detail = kwargs["event_detail"]
# 检查用户权限
token = get_token()
if token:
try:
r = verify_token(token, event_name, event_detail).json()
except Exception as err:
raise PermissionError(f"验证token出错，{err}")
if r.get("message") == "success":
# 如果有足够权限，调用原始函数
return func(*args, **kwargs)
else:
print(f"验证token出错，{r.text}")
raise PermissionError(f"{r['msg']}")
else:
raise PermissionError(ct.TOKEN_ERR_MSG)
wrapper.__doc__ = func.__doc__
return wrapper
return decorator
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725,417 | tushare.pro.data_pro | subs | null | def subs(token=''):
if token == '' or token is None:
token = upass.get_token()
from tushare.subs.ts_subs.subscribe import TsSubscribe
app = TsSubscribe(token=token)
return app
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725,427 | lotkavolterra_simulator.simulator | LVobserver | This class contains observational simulators
for a prey and predator populations.
Attributes
----------
Xtrue : array, double, dimension=n
true number of preys to be observed
Ytrue : array, double, dimension=n
true number of predators to be observed
X0 : double
initial condition: number of preys
Y0 : double
initial condition: number of predators
| class LVobserver(object):
"""This class contains observational simulators
for a prey and predator populations.
Attributes
----------
Xtrue : array, double, dimension=n
true number of preys to be observed
Ytrue : array, double, dimension=n
true number of predators to be observed
X0 : double
initial condition: number of preys
Y0 : double
initial condition: number of predators
"""
def __init__(self, Xtrue, Ytrue, X0, Y0):
self.Xtrue=Xtrue
self.Ytrue=Ytrue
self.X0=X0
self.Y0=Y0
@property
def tmax(self):
"""double: number of timesteps (each of length 1).
"""
return len(self.Xtrue)
@property
def t(self):
"""array, double: timestepping of the problem.
"""
import numpy as np
return np.arange(self.tmax)
def make_signal(self, **metaparams):
"""Simulate the signal.
Parameters
----------
model : int, optional, default=0
0= correct data model; 1=misspecified data model
Xefficiency : array, double, dimension=len(t)
detection efficiency of preys as a function of time
Yefficiency : array, double, dimension=len(t)
detection efficiency of preys as a function of time
P : double
rate of prey misses due to correlation between preys and predators
Q : double
rate of predator misses due to correlation between preys and predators
Returns
-------
Xsignal : array, double, dimension=n
unobserved signal for the number of preys
Ysignal : array, double, dimension=n
unobserved signal for the number of predators
"""
import numpy as np
t=self.t
X0=self.X0
Y0=self.Y0
Xtrue=self.Xtrue
Ytrue=self.Ytrue
Xsignal, Ysignal = self.Xtrue, self.Ytrue
if not "model" in metaparams or metaparams["model"] == 0:
# correct data model: The (unobserved) signal is a retarded and non-linear
# observation of the true underlying functions.
P=metaparams["obsP"]
Q=metaparams["obsQ"]
Xefficiency=metaparams["Xefficiency"]
Yefficiency=metaparams["Yefficiency"]
Xretard = np.roll(Xtrue,1)
Yretard = np.roll(Ytrue,1)
Xsignal = Xretard - P*Xretard*Yretard + Q*Xretard**2
Xsignal *= Xefficiency
Ysignal = Yretard + P*Xretard*Yretard - Q*Yretard**2
Ysignal *= Yefficiency
Xsignal[0] = X0
Ysignal[0] = Y0
elif "model" == 1:
# misspecified data model : consider that the measurement is a direct
# observation of the function.
pass
return Xsignal, Ysignal
def Dnoise_cov(self, **metaparams):
"""Covariance matrix for the demographic noise.
Demographic noise depends only on the observed population.
Parameters
----------
obsR : double
strength of demographic noise
Returns
-------
D : array, double, dimension=(2*n,2*n)
demographic noise covariance matrix
"""
import numpy as np
Xtrue=self.Xtrue
Ytrue=self.Ytrue
obsR=metaparams["obsR"]
D=obsR*np.diag(np.concatenate((Xtrue, Ytrue)))
return D
def make_demographic_noise(self, **metaparams):
"""Simulate demographic noise.
Demographic noise depends only on the observed population.
Parameters
----------
obsR : double
strength of demographic noise
Returns
-------
XDnoise : array, double, dimension=n
demographic noise for preys
YDnoise : array, double, dimension=n
demographic noise for predators
"""
import numpy as np
import scipy.stats as ss
Xtrue=self.Xtrue
Ytrue=self.Ytrue
obsR=metaparams["obsR"]
XDnoise = np.sqrt(obsR*Xtrue) * ss.multivariate_normal(mean=np.zeros_like(Xtrue)).rvs()
YDnoise = np.sqrt(obsR*Ytrue) * ss.multivariate_normal(mean=np.zeros_like(Xtrue)).rvs()
return XDnoise, YDnoise
def Onoise_cov(self, X, Y, **metaparams):
"""Covariance matrix for the observational noise.
Prey and predator populations introduce a noise to the other
population, and there is also a non-diagonal term
proportional to the geometric mean of both populations.
Parameters
----------
obsS : double
overall strength of observational noise
obsT : double
strength of non-diagonal term in observational noise
Returns
-------
O : array, double, dimension=(2*n,2*n)
observational noise covariance matrix
Note
----
Getting a positive semidefinite matrix should be enforced by user parameter choice.
"""
import numpy as np
obsS=metaparams["obsS"]
obsT=metaparams["obsT"]
O00=np.diag(Y)
O01=obsT*np.diag(np.sqrt(X*Y))
O10=obsT*np.diag(np.sqrt(X*Y))
O11=np.diag(X)
O=np.block([ [obsS*O00, obsS*O01], [obsS*O10, obsS*O11] ])
return O
def make_observational_noise(self, Xsignal, Ysignal, **metaparams):
"""Simulate observational noise.
Parameters
----------
obsS : double
overall strength of observational noise
obsT : double
strength of non-diagonal term in observational noise
Returns
-------
XOnoise : array, double, dimension=n
observational noise for preys
YOnoise : array, double, dimension=n
observational noise for predators
"""
import numpy as np
import scipy.stats as ss
from scipy.linalg import sqrtm
tmax=self.tmax
# Writing this in a single call to multivariate_normal causes numerical instabilities
XOnoise = np.zeros(tmax) # Pre-allocate the memory for XOnoise
YOnoise = np.zeros(tmax) # Pre-allocate the memory for YOnoise
for n in range(tmax-1):
O = self.Onoise_cov(np.array([Xsignal[n]]), np.array([Ysignal[n]]), **metaparams)
XOnoise[n], YOnoise[n] = ss.multivariate_normal(mean=[0,0], cov=O).rvs()
return XOnoise, YOnoise
def simulate_obs(self, Xsignal, Ysignal, **metaparams):
"""Simulate the observational process, assuming additive noise, i.e.
data = signal + noise.
Parameters
----------
Xsignal : array, double, dimension=n
unobserved signal for the number of preys
Ysignal : array, double, dimension=n
unobserved signal for the number of predators
model : int, optional, default=0
0= correct data model; 1=misspecified data model
obsR : double
strength of demographic noise
obsS : double
overall strength of observational noise
obsT : double
strength of non-diagonal term in observational noise
"""
import numpy as np
import scipy.stats as ss
tmax=self.tmax
# draw demographic noise
XDnoise, YDnoise = self.make_demographic_noise(**metaparams)
# observational noise
XOnoise, YOnoise = np.zeros_like(XDnoise), np.zeros_like(YDnoise)
if not "model" in metaparams or metaparams["model"] == 0:
# correct data model: draw observational noise
XOnoise, YOnoise = self.make_observational_noise(Xsignal, Ysignal, **metaparams)
elif "model" == 1:
# misspecified data model : only consider demographic noise
pass
# data = signal+noise
self.Xdata = Xsignal+XDnoise+XOnoise
self.Ydata = Ysignal+YDnoise+YOnoise
self.Xobs = np.arange(tmax)
self.Yobs = np.arange(tmax)
return Xsignal, XDnoise, XOnoise, self.Xdata, Ysignal, YDnoise, YOnoise, self.Ydata
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725,428 | lotkavolterra_simulator.simulator | Dnoise_cov | Covariance matrix for the demographic noise.
Demographic noise depends only on the observed population.
Parameters
----------
obsR : double
strength of demographic noise
Returns
-------
D : array, double, dimension=(2*n,2*n)
demographic noise covariance matrix
| def Dnoise_cov(self, **metaparams):
"""Covariance matrix for the demographic noise.
Demographic noise depends only on the observed population.
Parameters
----------
obsR : double
strength of demographic noise
Returns
-------
D : array, double, dimension=(2*n,2*n)
demographic noise covariance matrix
"""
import numpy as np
Xtrue=self.Xtrue
Ytrue=self.Ytrue
obsR=metaparams["obsR"]
D=obsR*np.diag(np.concatenate((Xtrue, Ytrue)))
return D
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725,429 | lotkavolterra_simulator.simulator | Onoise_cov | Covariance matrix for the observational noise.
Prey and predator populations introduce a noise to the other
population, and there is also a non-diagonal term
proportional to the geometric mean of both populations.
Parameters
----------
obsS : double
overall strength of observational noise
obsT : double
strength of non-diagonal term in observational noise
Returns
-------
O : array, double, dimension=(2*n,2*n)
observational noise covariance matrix
Note
----
Getting a positive semidefinite matrix should be enforced by user parameter choice.
| def Onoise_cov(self, X, Y, **metaparams):
"""Covariance matrix for the observational noise.
Prey and predator populations introduce a noise to the other
population, and there is also a non-diagonal term
proportional to the geometric mean of both populations.
Parameters
----------
obsS : double
overall strength of observational noise
obsT : double
strength of non-diagonal term in observational noise
Returns
-------
O : array, double, dimension=(2*n,2*n)
observational noise covariance matrix
Note
----
Getting a positive semidefinite matrix should be enforced by user parameter choice.
"""
import numpy as np
obsS=metaparams["obsS"]
obsT=metaparams["obsT"]
O00=np.diag(Y)
O01=obsT*np.diag(np.sqrt(X*Y))
O10=obsT*np.diag(np.sqrt(X*Y))
O11=np.diag(X)
O=np.block([ [obsS*O00, obsS*O01], [obsS*O10, obsS*O11] ])
return O
| (self, X, Y, **metaparams) | [
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725,430 | lotkavolterra_simulator.simulator | __init__ | null | def __init__(self, Xtrue, Ytrue, X0, Y0):
self.Xtrue=Xtrue
self.Ytrue=Ytrue
self.X0=X0
self.Y0=Y0
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725,431 | lotkavolterra_simulator.simulator | censor | Censor part of the data during periods
when preys are not observable.
Parameters
----------
mask : array, double, dimension=n
a mask containing zeros and ones
| def censor(self, **metaparams):
"""Censor part of the data during periods
when preys are not observable.
Parameters
----------
mask : array, double, dimension=n
a mask containing zeros and ones
"""
import numpy as np
mask=metaparams["mask"]
self.Xobs = self.Xobs[np.where(mask==1)]
self.Xdata = self.Xdata[np.where(mask==1)]
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725,432 | lotkavolterra_simulator.simulator | make_demographic_noise | Simulate demographic noise.
Demographic noise depends only on the observed population.
Parameters
----------
obsR : double
strength of demographic noise
Returns
-------
XDnoise : array, double, dimension=n
demographic noise for preys
YDnoise : array, double, dimension=n
demographic noise for predators
| def make_demographic_noise(self, **metaparams):
"""Simulate demographic noise.
Demographic noise depends only on the observed population.
Parameters
----------
obsR : double
strength of demographic noise
Returns
-------
XDnoise : array, double, dimension=n
demographic noise for preys
YDnoise : array, double, dimension=n
demographic noise for predators
"""
import numpy as np
import scipy.stats as ss
Xtrue=self.Xtrue
Ytrue=self.Ytrue
obsR=metaparams["obsR"]
XDnoise = np.sqrt(obsR*Xtrue) * ss.multivariate_normal(mean=np.zeros_like(Xtrue)).rvs()
YDnoise = np.sqrt(obsR*Ytrue) * ss.multivariate_normal(mean=np.zeros_like(Xtrue)).rvs()
return XDnoise, YDnoise
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725,433 | lotkavolterra_simulator.simulator | make_observational_noise | Simulate observational noise.
Parameters
----------
obsS : double
overall strength of observational noise
obsT : double
strength of non-diagonal term in observational noise
Returns
-------
XOnoise : array, double, dimension=n
observational noise for preys
YOnoise : array, double, dimension=n
observational noise for predators
| def make_observational_noise(self, Xsignal, Ysignal, **metaparams):
"""Simulate observational noise.
Parameters
----------
obsS : double
overall strength of observational noise
obsT : double
strength of non-diagonal term in observational noise
Returns
-------
XOnoise : array, double, dimension=n
observational noise for preys
YOnoise : array, double, dimension=n
observational noise for predators
"""
import numpy as np
import scipy.stats as ss
from scipy.linalg import sqrtm
tmax=self.tmax
# Writing this in a single call to multivariate_normal causes numerical instabilities
XOnoise = np.zeros(tmax) # Pre-allocate the memory for XOnoise
YOnoise = np.zeros(tmax) # Pre-allocate the memory for YOnoise
for n in range(tmax-1):
O = self.Onoise_cov(np.array([Xsignal[n]]), np.array([Ysignal[n]]), **metaparams)
XOnoise[n], YOnoise[n] = ss.multivariate_normal(mean=[0,0], cov=O).rvs()
return XOnoise, YOnoise
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] |
725,434 | lotkavolterra_simulator.simulator | make_signal | Simulate the signal.
Parameters
----------
model : int, optional, default=0
0= correct data model; 1=misspecified data model
Xefficiency : array, double, dimension=len(t)
detection efficiency of preys as a function of time
Yefficiency : array, double, dimension=len(t)
detection efficiency of preys as a function of time
P : double
rate of prey misses due to correlation between preys and predators
Q : double
rate of predator misses due to correlation between preys and predators
Returns
-------
Xsignal : array, double, dimension=n
unobserved signal for the number of preys
Ysignal : array, double, dimension=n
unobserved signal for the number of predators
| def make_signal(self, **metaparams):
"""Simulate the signal.
Parameters
----------
model : int, optional, default=0
0= correct data model; 1=misspecified data model
Xefficiency : array, double, dimension=len(t)
detection efficiency of preys as a function of time
Yefficiency : array, double, dimension=len(t)
detection efficiency of preys as a function of time
P : double
rate of prey misses due to correlation between preys and predators
Q : double
rate of predator misses due to correlation between preys and predators
Returns
-------
Xsignal : array, double, dimension=n
unobserved signal for the number of preys
Ysignal : array, double, dimension=n
unobserved signal for the number of predators
"""
import numpy as np
t=self.t
X0=self.X0
Y0=self.Y0
Xtrue=self.Xtrue
Ytrue=self.Ytrue
Xsignal, Ysignal = self.Xtrue, self.Ytrue
if not "model" in metaparams or metaparams["model"] == 0:
# correct data model: The (unobserved) signal is a retarded and non-linear
# observation of the true underlying functions.
P=metaparams["obsP"]
Q=metaparams["obsQ"]
Xefficiency=metaparams["Xefficiency"]
Yefficiency=metaparams["Yefficiency"]
Xretard = np.roll(Xtrue,1)
Yretard = np.roll(Ytrue,1)
Xsignal = Xretard - P*Xretard*Yretard + Q*Xretard**2
Xsignal *= Xefficiency
Ysignal = Yretard + P*Xretard*Yretard - Q*Yretard**2
Ysignal *= Yefficiency
Xsignal[0] = X0
Ysignal[0] = Y0
elif "model" == 1:
# misspecified data model : consider that the measurement is a direct
# observation of the function.
pass
return Xsignal, Ysignal
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725,435 | lotkavolterra_simulator.simulator | observe | Simulate the observation process using a given data model.
Parameters
----------
model : int, optional, default=0
0= correct data model; 1=misspecified data model
threshold : double
maximum number of individuals (preys or predators) that can be observed
mask : array, double, dimension=n
a mask containing zeros and ones
P : double
rate of prey misses due to correlation between preys and predators
Q : double
rate of predator misses due to correlation between preys and predators
R : double
strength of demographic noise
S : double
overall strength of observational noise
T : double
strength of non-diagonal term in observational noise
| def observe(self, **metaparams):
"""Simulate the observation process using a given data model.
Parameters
----------
model : int, optional, default=0
0= correct data model; 1=misspecified data model
threshold : double
maximum number of individuals (preys or predators) that can be observed
mask : array, double, dimension=n
a mask containing zeros and ones
P : double
rate of prey misses due to correlation between preys and predators
Q : double
rate of predator misses due to correlation between preys and predators
R : double
strength of demographic noise
S : double
overall strength of observational noise
T : double
strength of non-diagonal term in observational noise
"""
import numpy as np
# Simulate the observation process using a given data model
# signal
Xsignal, Ysignal = self.make_signal(**metaparams)
# simulate observation using correct data model
self.simulate_obs(Xsignal, Ysignal, **metaparams)
# censor and threshold data
self.censor(**metaparams)
self.threshold(**metaparams)
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725,436 | lotkavolterra_simulator.simulator | simulate_obs | Simulate the observational process, assuming additive noise, i.e.
data = signal + noise.
Parameters
----------
Xsignal : array, double, dimension=n
unobserved signal for the number of preys
Ysignal : array, double, dimension=n
unobserved signal for the number of predators
model : int, optional, default=0
0= correct data model; 1=misspecified data model
obsR : double
strength of demographic noise
obsS : double
overall strength of observational noise
obsT : double
strength of non-diagonal term in observational noise
| def simulate_obs(self, Xsignal, Ysignal, **metaparams):
"""Simulate the observational process, assuming additive noise, i.e.
data = signal + noise.
Parameters
----------
Xsignal : array, double, dimension=n
unobserved signal for the number of preys
Ysignal : array, double, dimension=n
unobserved signal for the number of predators
model : int, optional, default=0
0= correct data model; 1=misspecified data model
obsR : double
strength of demographic noise
obsS : double
overall strength of observational noise
obsT : double
strength of non-diagonal term in observational noise
"""
import numpy as np
import scipy.stats as ss
tmax=self.tmax
# draw demographic noise
XDnoise, YDnoise = self.make_demographic_noise(**metaparams)
# observational noise
XOnoise, YOnoise = np.zeros_like(XDnoise), np.zeros_like(YDnoise)
if not "model" in metaparams or metaparams["model"] == 0:
# correct data model: draw observational noise
XOnoise, YOnoise = self.make_observational_noise(Xsignal, Ysignal, **metaparams)
elif "model" == 1:
# misspecified data model : only consider demographic noise
pass
# data = signal+noise
self.Xdata = Xsignal+XDnoise+XOnoise
self.Ydata = Ysignal+YDnoise+YOnoise
self.Xobs = np.arange(tmax)
self.Yobs = np.arange(tmax)
return Xsignal, XDnoise, XOnoise, self.Xdata, Ysignal, YDnoise, YOnoise, self.Ydata
| (self, Xsignal, Ysignal, **metaparams) | [
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] |
725,437 | lotkavolterra_simulator.simulator | threshold | Threshold the data, excluding negative measurements
and measurements above a detection maximum.
Parameters
----------
threshold : double
maximum number of individuals (preys or predators) that can be observed
| def threshold(self, **metaparams):
"""Threshold the data, excluding negative measurements
and measurements above a detection maximum.
Parameters
----------
threshold : double
maximum number of individuals (preys or predators) that can be observed
"""
import numpy as np
if not "model" in metaparams or metaparams["model"] == 0:
# correct data model: threshold data
threshold=metaparams["threshold"]
self.Xdata[np.where(self.Xdata>threshold)]=threshold
self.Ydata[np.where(self.Ydata>threshold)]=threshold
self.Xdata[np.where(self.Xdata<0)]=0
self.Ydata[np.where(self.Ydata<0)]=0
elif "model" == 1:
# misspecified data model : do not threshold data
pass
| (self, **metaparams) | [
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725,438 | lotkavolterra_simulator.simulator | LVsimulator | This class contains a basic Lotka-Volterra simulator
and the observational simulator.
X0 and Y0 are inputs and are the initial populations of each species
alpha, beta, gamma, delta are inputs and problem parameters
Attributes
----------
X0 : double
initial condition: number of preys
Y0 : double
initial condition: number of predators
alpha : double
intrinsic reproduction rate of preys (independent of the number of predators)
beta : double
prey mortality rate due to predators encountered
gamma : double
predator reproduction rate according to preys encountered and eaten
delta : double
intrinsic predator mortality rate (independent of the number of prey)
| class LVsimulator(object):
"""This class contains a basic Lotka-Volterra simulator
and the observational simulator.
X0 and Y0 are inputs and are the initial populations of each species
alpha, beta, gamma, delta are inputs and problem parameters
Attributes
----------
X0 : double
initial condition: number of preys
Y0 : double
initial condition: number of predators
alpha : double
intrinsic reproduction rate of preys (independent of the number of predators)
beta : double
prey mortality rate due to predators encountered
gamma : double
predator reproduction rate according to preys encountered and eaten
delta : double
intrinsic predator mortality rate (independent of the number of prey)
"""
def __init__(self, X0, Y0, alpha, beta, gamma, delta):
self.X0=X0
self.Y0=Y0
self.alpha=alpha
self.beta=beta
self.gamma=gamma
self.delta=delta
def EEuler(self, t):
"""Solves Lotka-Volterra equations for one prey and one predator species using
the explicit Euler method.
Parameters
----------
t : array, double, dimension=n
array of time values where we approximate X and Y values
timestep at each iteration is given by t[n+1] - t[n].
Returns
-------
X : array, double, dimension=n
number of preys
Y : array, double, dimension=n
number of predators
"""
import numpy as np
X = np.zeros(len(t)) # Pre-allocate the memory for X
Y = np.zeros(len(t)) # Pre-allocate the memory for Y
X[0] = self.X0
Y[0] = self.Y0
for n in range(len(t)-1):
dt = t[n+1] - t[n]
X[n+1] = X[n]*(1 + self.alpha*dt - self.beta*dt*Y[n])
Y[n+1] = Y[n]*(1 - self.gamma*dt + self.delta*dt*X[n])
return X, Y
@property
def omega(self):
"""array, double, dimension=4 : vector of parameters
"""
import numpy as np
return np.array((self.alpha, self.beta, self.gamma, self.delta))
@staticmethod
def get_XY(t_s, tres, Xth, Yth):
"""two array, double, dimension=2*n: subsamples in time to get X and Y.
"""
import numpy as np
X=np.array([Xth[tres*n] for n in range(len(t_s))])
Y=np.array([Yth[tres*n] for n in range(len(t_s))])
return X, Y
@staticmethod
def get_theta(X, Y):
"""array, double, dimension=2*n: concatenates X and Y.
"""
import numpy as np
return np.concatenate((X, Y))
def compute_theta(self, omega, t, t_s, tres):
"""Solves the Lotka-Volterra system of equations and
returns theta, the latent function that is the next layer
of the Bayesian hierarchical model.
Parameters
----------
t : array, double, dimension=n
array of time values where we approximate X and Y values
timestep at each iteration is given by t[n+1] - t[n].
t_s : array, double, dimension=S
array of the support time vales where theta is defined
tres : double
time resolution, defined such that:
t = np.linspace(0,tmax-1,tmax*tres+1)
t_s = np.arange(tmax)
Returns
-------
theta : array, double, dimension=S
vector of values of the latent function at t_s
"""
LV = LVsimulator(self.X0, self.Y0, omega[0], omega[1], omega[2], omega[3])
Xtheory, Ytheory = LV.EEuler(t)
X, Y = LV.get_XY(t_s, tres, Xtheory, Ytheory)
theta = LV.get_theta(X, Y)
return theta
def compute_dtheta_domega(self, t, t_s, tres, Delta_omega):
"""Computes the gradient of the latent function theta with respect to
the parameters omega, using finite differences.
Parameters
----------
t : array, double, dimension=n
array of time values where we approximate X and Y values
timestep at each iteration is given by t[n+1] - t[n].
t_s : array, double, dimension=S
array of the support time vales where theta is defined
tres : double
time resolution, defined such that:
t = np.linspace(0,tmax-1,tmax*tres+1)
t_s = np.arange(tmax)
Delta_omega : double
step size for finite differences
Returns
-------
grad_theta : array, double, dimension=(2*S, 4)
gradient of theta with respect to omega
"""
import numpy as np
omega0 = self.omega
tmax = len(t_s)
grad_theta = np.zeros((2*tmax,4))
theta_0 = self.compute_theta(omega0, t, t_s, tres)
for n in range(4):
omega_p1, omega_p2, omega_p3, omega_m1, omega_m2, omega_m3 = np.copy(omega0), np.copy(omega0), np.copy(omega0), np.copy(omega0), np.copy(omega0), np.copy(omega0)
omega_p1[n] += 1*Delta_omega
omega_p2[n] += 2*Delta_omega
omega_p3[n] += 3*Delta_omega
omega_m1[n] -= 1*Delta_omega
omega_m2[n] -= 2*Delta_omega
omega_m3[n] -= 3*Delta_omega
theta_p1 = self.compute_theta(omega_p1, t, t_s, tres)
theta_p2 = self.compute_theta(omega_p2, t, t_s, tres)
theta_p3 = self.compute_theta(omega_p3, t, t_s, tres)
theta_m1 = self.compute_theta(omega_m1, t, t_s, tres)
theta_m2 = self.compute_theta(omega_m2, t, t_s, tres)
theta_m3 = self.compute_theta(omega_m3, t, t_s, tres)
#grad_theta[:,n]=(theta_p1 - theta_0)/Delta_omega
#grad_theta[:,n]=(-1/2.*theta_m1 + 1/2.*theta_p1)/Delta_omega
#grad_theta[:,n]=(1/12.*theta_m2 - 2/3.*theta_m1 + 2/3.*theta_p1 - 1/12.*theta_p2)/Delta_omega
grad_theta[:,n]=(-1/60.*theta_m3 + 3/20.*theta_m2 - 3/4.*theta_m1 + 3/4.*theta_p1 - 3/20.*theta_p2 + 1/60.*theta_p3)/Delta_omega
return grad_theta
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725,439 | lotkavolterra_simulator.simulator | EEuler | Solves Lotka-Volterra equations for one prey and one predator species using
the explicit Euler method.
Parameters
----------
t : array, double, dimension=n
array of time values where we approximate X and Y values
timestep at each iteration is given by t[n+1] - t[n].
Returns
-------
X : array, double, dimension=n
number of preys
Y : array, double, dimension=n
number of predators
| def EEuler(self, t):
"""Solves Lotka-Volterra equations for one prey and one predator species using
the explicit Euler method.
Parameters
----------
t : array, double, dimension=n
array of time values where we approximate X and Y values
timestep at each iteration is given by t[n+1] - t[n].
Returns
-------
X : array, double, dimension=n
number of preys
Y : array, double, dimension=n
number of predators
"""
import numpy as np
X = np.zeros(len(t)) # Pre-allocate the memory for X
Y = np.zeros(len(t)) # Pre-allocate the memory for Y
X[0] = self.X0
Y[0] = self.Y0
for n in range(len(t)-1):
dt = t[n+1] - t[n]
X[n+1] = X[n]*(1 + self.alpha*dt - self.beta*dt*Y[n])
Y[n+1] = Y[n]*(1 - self.gamma*dt + self.delta*dt*X[n])
return X, Y
| (self, t) | [
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725,440 | lotkavolterra_simulator.simulator | __init__ | null | def __init__(self, X0, Y0, alpha, beta, gamma, delta):
self.X0=X0
self.Y0=Y0
self.alpha=alpha
self.beta=beta
self.gamma=gamma
self.delta=delta
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725,441 | lotkavolterra_simulator.simulator | compute_dtheta_domega | Computes the gradient of the latent function theta with respect to
the parameters omega, using finite differences.
Parameters
----------
t : array, double, dimension=n
array of time values where we approximate X and Y values
timestep at each iteration is given by t[n+1] - t[n].
t_s : array, double, dimension=S
array of the support time vales where theta is defined
tres : double
time resolution, defined such that:
t = np.linspace(0,tmax-1,tmax*tres+1)
t_s = np.arange(tmax)
Delta_omega : double
step size for finite differences
Returns
-------
grad_theta : array, double, dimension=(2*S, 4)
gradient of theta with respect to omega
| def compute_dtheta_domega(self, t, t_s, tres, Delta_omega):
"""Computes the gradient of the latent function theta with respect to
the parameters omega, using finite differences.
Parameters
----------
t : array, double, dimension=n
array of time values where we approximate X and Y values
timestep at each iteration is given by t[n+1] - t[n].
t_s : array, double, dimension=S
array of the support time vales where theta is defined
tres : double
time resolution, defined such that:
t = np.linspace(0,tmax-1,tmax*tres+1)
t_s = np.arange(tmax)
Delta_omega : double
step size for finite differences
Returns
-------
grad_theta : array, double, dimension=(2*S, 4)
gradient of theta with respect to omega
"""
import numpy as np
omega0 = self.omega
tmax = len(t_s)
grad_theta = np.zeros((2*tmax,4))
theta_0 = self.compute_theta(omega0, t, t_s, tres)
for n in range(4):
omega_p1, omega_p2, omega_p3, omega_m1, omega_m2, omega_m3 = np.copy(omega0), np.copy(omega0), np.copy(omega0), np.copy(omega0), np.copy(omega0), np.copy(omega0)
omega_p1[n] += 1*Delta_omega
omega_p2[n] += 2*Delta_omega
omega_p3[n] += 3*Delta_omega
omega_m1[n] -= 1*Delta_omega
omega_m2[n] -= 2*Delta_omega
omega_m3[n] -= 3*Delta_omega
theta_p1 = self.compute_theta(omega_p1, t, t_s, tres)
theta_p2 = self.compute_theta(omega_p2, t, t_s, tres)
theta_p3 = self.compute_theta(omega_p3, t, t_s, tres)
theta_m1 = self.compute_theta(omega_m1, t, t_s, tres)
theta_m2 = self.compute_theta(omega_m2, t, t_s, tres)
theta_m3 = self.compute_theta(omega_m3, t, t_s, tres)
#grad_theta[:,n]=(theta_p1 - theta_0)/Delta_omega
#grad_theta[:,n]=(-1/2.*theta_m1 + 1/2.*theta_p1)/Delta_omega
#grad_theta[:,n]=(1/12.*theta_m2 - 2/3.*theta_m1 + 2/3.*theta_p1 - 1/12.*theta_p2)/Delta_omega
grad_theta[:,n]=(-1/60.*theta_m3 + 3/20.*theta_m2 - 3/4.*theta_m1 + 3/4.*theta_p1 - 3/20.*theta_p2 + 1/60.*theta_p3)/Delta_omega
return grad_theta
| (self, t, t_s, tres, Delta_omega) | [
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] |
725,442 | lotkavolterra_simulator.simulator | compute_theta | Solves the Lotka-Volterra system of equations and
returns theta, the latent function that is the next layer
of the Bayesian hierarchical model.
Parameters
----------
t : array, double, dimension=n
array of time values where we approximate X and Y values
timestep at each iteration is given by t[n+1] - t[n].
t_s : array, double, dimension=S
array of the support time vales where theta is defined
tres : double
time resolution, defined such that:
t = np.linspace(0,tmax-1,tmax*tres+1)
t_s = np.arange(tmax)
Returns
-------
theta : array, double, dimension=S
vector of values of the latent function at t_s
| def compute_theta(self, omega, t, t_s, tres):
"""Solves the Lotka-Volterra system of equations and
returns theta, the latent function that is the next layer
of the Bayesian hierarchical model.
Parameters
----------
t : array, double, dimension=n
array of time values where we approximate X and Y values
timestep at each iteration is given by t[n+1] - t[n].
t_s : array, double, dimension=S
array of the support time vales where theta is defined
tres : double
time resolution, defined such that:
t = np.linspace(0,tmax-1,tmax*tres+1)
t_s = np.arange(tmax)
Returns
-------
theta : array, double, dimension=S
vector of values of the latent function at t_s
"""
LV = LVsimulator(self.X0, self.Y0, omega[0], omega[1], omega[2], omega[3])
Xtheory, Ytheory = LV.EEuler(t)
X, Y = LV.get_XY(t_s, tres, Xtheory, Ytheory)
theta = LV.get_theta(X, Y)
return theta
| (self, omega, t, t_s, tres) | [
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] |
725,443 | lotkavolterra_simulator.simulator | get_XY | two array, double, dimension=2*n: subsamples in time to get X and Y.
| @staticmethod
def get_XY(t_s, tres, Xth, Yth):
"""two array, double, dimension=2*n: subsamples in time to get X and Y.
"""
import numpy as np
X=np.array([Xth[tres*n] for n in range(len(t_s))])
Y=np.array([Yth[tres*n] for n in range(len(t_s))])
return X, Y
| (t_s, tres, Xth, Yth) | [
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] |
725,444 | lotkavolterra_simulator.simulator | get_theta | array, double, dimension=2*n: concatenates X and Y.
| @staticmethod
def get_theta(X, Y):
"""array, double, dimension=2*n: concatenates X and Y.
"""
import numpy as np
return np.concatenate((X, Y))
| (X, Y) | [
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725,446 | splitutils.core.splitutils | binning |
bins numeric data.
If X is one-dimensional:
binning is done either intrinsically into nbins classes
based on an equidistant percentile split, or extrinsically
by using the lower_boundaries values.
If X is two-dimensional
binning is done by kmeans clustering into nbins clusters
Args:
x: (list, np.array) with numeric data.
nbins: (int) number of bins
lower_boundaries: (list) of lower bin boundaries.
If y is 1-dim and lower_boundaries is provided, nbins will be ignored
and y is binned extrinsically. The first value of lower_boundaries
is always corrected not to be higher than min(y).
seed: (int) random seed for kmeans
Returns:
c: (np.array) integers as bin IDs
| def binning(x: Union[list, np.array],
nbins: int = 2,
lower_boundaries: Union[list, np.array, dict] = None,
seed: int = 42) -> np.array:
'''
bins numeric data.
If X is one-dimensional:
binning is done either intrinsically into nbins classes
based on an equidistant percentile split, or extrinsically
by using the lower_boundaries values.
If X is two-dimensional
binning is done by kmeans clustering into nbins clusters
Args:
x: (list, np.array) with numeric data.
nbins: (int) number of bins
lower_boundaries: (list) of lower bin boundaries.
If y is 1-dim and lower_boundaries is provided, nbins will be ignored
and y is binned extrinsically. The first value of lower_boundaries
is always corrected not to be higher than min(y).
seed: (int) random seed for kmeans
Returns:
c: (np.array) integers as bin IDs
'''
assert ((nbins is not None) or (lower_boundaries is not None)), \
"One of nbins or lower_boundaries must be set."
x = np.array(x, dtype=float)
assert ((nbins is not None) or x.ndim == 1), \
"For 2-dimensional data input nbins must be set for KMeans clustering."
if x.ndim == 1:
# 1-dim array
if lower_boundaries is None:
# intrinsic binning by equidistant percentiles
prct = np.linspace(0, 100, nbins+1)
lower_boundaries = np.percentile(x, prct)
lower_boundaries = lower_boundaries[0:nbins]
else:
# extrinsic binning
# make sure that entire range of x is covered
lower_boundaries[0] = min(lower_boundaries[0], np.min(x))
# binned array
c = np.zeros(len(x), dtype=int)
for i in range(1, len(lower_boundaries)):
c[x >= lower_boundaries[i]] = i
else:
# 2-dim array
# centering+scaling
sca = StandardScaler()
xs = sca.fit_transform(x)
# clustering
mod = KMeans(n_clusters=nbins, init='k-means++', max_iter=300, tol=0.0001,
random_state=seed, algorithm='lloyd', n_init=1)
mod.fit(xs)
c = mod.predict(xs)
return c
| (x: Union[list, <built-in function array>], nbins: int = 2, lower_boundaries: Union[list, <built-in function array>, dict, NoneType] = None, seed: int = 42) -> <built-in function array> | [
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725,448 | splitutils.core.splitutils | optimize_traindevtest_split | optimize group-disjunct split into training, dev, and test set, which is guided by:
- disjunct split of values in SPLIT_ON
- stratification by all keys in STRATIFY_ON (targets and groupings)
- test set proportion in X should be close to test_size (which is the test
proportion in set(split_on))
Score to be minimized: (sum_v[w(v) * max_irad(v)] + w(d) * max_d) / (sum_v[w(v)] + w(d))
(v: variables to be stratified on
w(v): their weight
max_irad(v): maximum information radius of reference distribution of classes in v and
- dev set distribution,
- test set distribution
N(v): number of stratification variables
max_d: maximum of absolute difference between dev and test sizes of X and set(split_on)
w(d): its weight
Args:
X: (np.array or pd.DataFrame) of features
y: (np.array) of targets of length N
if type(y[0]) in ["str", "int"]: y is assumed to be categorical, so that it is additionally
tested that all partitions cover all classes. Else y is assumed to be numeric and no
coverage test is done.
split_on: (np.array) list of length N with grouping variable (e.g. speaker IDs),
on which the group-disjunct split is to be performed. Must be categorical.
stratify_on: (dict) Dict-keys are variable names (targets and/or further groupings)
the split should be stratified on (groupings could e.g. be sex, age class, etc).
Dict-Values are np.array-s of length N that contain the variable values. All
variables must be categorical.
weight: (dict) weight for each variable in stratify_on. Defines their amount of
contribution to the optimization score. Uniform weighting by default. Additional
key: "size_diff" defines how the corresponding size differences should be weighted.
dev_size: (float) proportion in set(split_on) for dev set, e.g. 10% of speakers
to be held-out
test_size: (float) test proportion in set(split_on) for test set
k: (int) number of different splits to be tried out
seed: (int) random seed
Returns:
train_i: (np.array) train set indices in X
dev_i: (np.array) dev set indices in X
test_i: (np.array) test set indices in X
info: (dict) detail information about reference and achieved prob distributions
"dev_size_in_spliton": intended grouping dev_size
"dev_size_in_X": optimized dev proportion of observations in X
"test_size_in_spliton": intended grouping test_size
"test_size_in_X": optimized test proportion of observations in X
"p_ref_{c}": reference class distribution calculated from stratify_on[c]
"p_dev_{c}": dev set class distribution calculated from stratify_on[c][dev_i]
"p_test_{c}": test set class distribution calculated from stratify_on[c][test_i]
| def optimize_traindevtest_split(
X: Union[np.array, pd.DataFrame],
y: Union[np.array, list],
split_on: Union[np.array, list],
stratify_on: dict,
weight: dict = None,
dev_size: float = .1,
test_size: float = .1,
k: int = 30,
seed: int = 42) -> Tuple[np.array, np.array, np.array, dict]:
''' optimize group-disjunct split into training, dev, and test set, which is guided by:
- disjunct split of values in SPLIT_ON
- stratification by all keys in STRATIFY_ON (targets and groupings)
- test set proportion in X should be close to test_size (which is the test
proportion in set(split_on))
Score to be minimized: (sum_v[w(v) * max_irad(v)] + w(d) * max_d) / (sum_v[w(v)] + w(d))
(v: variables to be stratified on
w(v): their weight
max_irad(v): maximum information radius of reference distribution of classes in v and
- dev set distribution,
- test set distribution
N(v): number of stratification variables
max_d: maximum of absolute difference between dev and test sizes of X and set(split_on)
w(d): its weight
Args:
X: (np.array or pd.DataFrame) of features
y: (np.array) of targets of length N
if type(y[0]) in ["str", "int"]: y is assumed to be categorical, so that it is additionally
tested that all partitions cover all classes. Else y is assumed to be numeric and no
coverage test is done.
split_on: (np.array) list of length N with grouping variable (e.g. speaker IDs),
on which the group-disjunct split is to be performed. Must be categorical.
stratify_on: (dict) Dict-keys are variable names (targets and/or further groupings)
the split should be stratified on (groupings could e.g. be sex, age class, etc).
Dict-Values are np.array-s of length N that contain the variable values. All
variables must be categorical.
weight: (dict) weight for each variable in stratify_on. Defines their amount of
contribution to the optimization score. Uniform weighting by default. Additional
key: "size_diff" defines how the corresponding size differences should be weighted.
dev_size: (float) proportion in set(split_on) for dev set, e.g. 10% of speakers
to be held-out
test_size: (float) test proportion in set(split_on) for test set
k: (int) number of different splits to be tried out
seed: (int) random seed
Returns:
train_i: (np.array) train set indices in X
dev_i: (np.array) dev set indices in X
test_i: (np.array) test set indices in X
info: (dict) detail information about reference and achieved prob distributions
"dev_size_in_spliton": intended grouping dev_size
"dev_size_in_X": optimized dev proportion of observations in X
"test_size_in_spliton": intended grouping test_size
"test_size_in_X": optimized test proportion of observations in X
"p_ref_{c}": reference class distribution calculated from stratify_on[c]
"p_dev_{c}": dev set class distribution calculated from stratify_on[c][dev_i]
"p_test_{c}": test set class distribution calculated from stratify_on[c][test_i]
'''
# data size
N = len(y)
# size checks
assert size_check(stratify_on, N), \
f"all stratify_on arrays must have length {N}"
assert size_check(split_on, N), \
f"split_on array must have length {N}"
# categorical target: get number of classes for coverage test
if is_categorical(y[0]):
nc = len(set(y))
else:
nc = None
# adjusted dev_size after having split off the test set
dev_size_adj = (dev_size * N) / (N - test_size * N)
# split all into train/dev vs test
gss_o = GroupShuffleSplit(n_splits=k, test_size=test_size,
random_state=seed)
# split train/dev into train vs dev
gss_i = GroupShuffleSplit(n_splits=k, test_size=dev_size_adj,
random_state=seed)
# set weight defaults
if weight is None:
weight = {}
for c in stratify_on.keys():
if c not in weight:
weight[c] = 1
if "size_diff" not in weight:
weight["size_diff"] = 1
# stratification reference distributions calculated on stratify_on
p_ref = {}
for c in stratify_on:
p_ref[c] = class_prob(stratify_on[c])
# best train/dev/test indices in X; best associated score
train_i, dev_i, test_i, best_sco = None, None, None, np.inf
# full target coverage in all partitions
full_target_coverage = False
# for appropriate subsetting
xtype = type(X)
# brute-force optimization of SPLIT_ON split
# outer loop *_o: splitting into train/dev and test
# inner loop *_i: spltting into train and dev
for tri_o, tei_o in gss_o.split(X, y, split_on):
# current train/dev partition
if xtype == pd.DataFrame:
X_i = X.iloc[tri_o]
else:
X_i = X[tri_o]
y_i = y[tri_o]
split_on_i = split_on[tri_o]
for tri_i, tei_i in gss_i.split(X_i, y_i, split_on_i):
# all classes maintained in all partitions?
if nc:
nc_train = len(set(y[tri_o[tri_i]]))
nc_dev = len(set(y[tri_o[tei_i]]))
nc_test = len(set(y[tei_o]))
if min(nc_train, nc_dev, nc_test) < nc:
continue
full_target_coverage = True
sco = calc_split_score(test_i=tei_o,
stratify_on=stratify_on,
weight=weight, p_ref=p_ref,
N=N, test_size=test_size,
dev_i=tri_o[tei_i],
dev_size=dev_size_adj)
if sco < best_sco:
best_sco = sco
test_i = tei_o
train_i = tri_o[tri_i]
dev_i = tri_o[tei_i]
if test_i is None:
sys.exit(exit_message(full_target_coverage, "dev and test"))
# matching info
info = {"score": best_sco,
"size_devset_in_spliton": dev_size,
"size_devset_in_X": np.round(len(dev_i) / N, 2),
"size_testset_in_spliton": test_size,
"size_testset_in_X": np.round(len(test_i) / N, 2)}
for c in p_ref:
info[f"p_{c}_ref"] = p_ref[c]
info[f"p_{c}_dev"] = class_prob(stratify_on[c][dev_i])
info[f"p_{c}_test"] = class_prob(stratify_on[c][test_i])
return train_i, dev_i, test_i, info
| (X: Union[<built-in function array>, pandas.core.frame.DataFrame], y: Union[<built-in function array>, list], split_on: Union[<built-in function array>, list], stratify_on: dict, weight: Optional[dict] = None, dev_size: float = 0.1, test_size: float = 0.1, k: int = 30, seed: int = 42) -> Tuple[<built-in function array>, <built-in function array>, <built-in function array>, dict] | [
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725,449 | splitutils.core.splitutils | optimize_traintest_split | optimize group-disjunct split which is guided by:
- disjunct split of values in SPLIT_ON
- stratification by all keys in STRATIFY_ON (targets and groupings)
- test set proportion in X should be close to test_size (which is the test
proportion in set(split_on))
Score to be minimized: (sum_v[w(v) * irad(v)] + w(d) * d) / (sum_v[w(v)] + w(d))
(v: variables to be stratified on
w(v): their weight
irad(v): information radius between reference distribution of classes in v
and test set distribution
N(v): number of stratification variables
d: absolute difference between test sizes of X and set(split_on)
w(d): its weight
Args:
X: (np.array or pd.DataFrame) of features
y: (np.array) of targets of length N
if type(y[0]) in ["str", "int"]: y is assumed to be categorical, so that it is additionally
tested that all partitions cover all classes. Else y is assumed to be numeric and no
coverage test is done.
split_on: (np.array) list of length N with grouping variable (e.g. speaker IDs),
on which the group-disjunct split is to be performed. Must be categorical.
stratify_on: (dict) Dict-keys are variable names (targets and/or further groupings)
the split should be stratified on (groupings could e.g. be sex, age class, etc).
Dict-Values are np.array-s of length N that contain the variable values. All
variables must be categorical.
weight: (dict) weight for each variable in stratify_on. Defines their amount of
contribution to the optimization score. Uniform weighting by default. Additional
key: "size_diff" defines how test size diff should be weighted.
test_size: (float) test proportion in set(split_on), e.g. 10% of speakers to be held-out
k: (int) number of different splits to be tried out
seed: (int) random seed
Returns:
train_i: (np.array) train set indices in X
test_i: (np.array) test set indices in X
info: (dict) detail information about reference and achieved prob distributions
"size_testset_in_spliton": intended test_size
"size_testset_in_X": optimized test proportion in X
"p_ref_{c}": reference class distribution calculated from stratify_on[c]
"p_test_{c}": test set class distribution calculated from stratify_on[c][test_i]
| def optimize_traintest_split(
X: Union[np.array, pd.DataFrame],
y: Union[np.array, list],
split_on: Union[np.array, list],
stratify_on: dict,
weight: dict = None,
test_size: float = .1,
k: int = 30,
seed: int = 42) -> Tuple[np.array, np.array, dict]:
''' optimize group-disjunct split which is guided by:
- disjunct split of values in SPLIT_ON
- stratification by all keys in STRATIFY_ON (targets and groupings)
- test set proportion in X should be close to test_size (which is the test
proportion in set(split_on))
Score to be minimized: (sum_v[w(v) * irad(v)] + w(d) * d) / (sum_v[w(v)] + w(d))
(v: variables to be stratified on
w(v): their weight
irad(v): information radius between reference distribution of classes in v
and test set distribution
N(v): number of stratification variables
d: absolute difference between test sizes of X and set(split_on)
w(d): its weight
Args:
X: (np.array or pd.DataFrame) of features
y: (np.array) of targets of length N
if type(y[0]) in ["str", "int"]: y is assumed to be categorical, so that it is additionally
tested that all partitions cover all classes. Else y is assumed to be numeric and no
coverage test is done.
split_on: (np.array) list of length N with grouping variable (e.g. speaker IDs),
on which the group-disjunct split is to be performed. Must be categorical.
stratify_on: (dict) Dict-keys are variable names (targets and/or further groupings)
the split should be stratified on (groupings could e.g. be sex, age class, etc).
Dict-Values are np.array-s of length N that contain the variable values. All
variables must be categorical.
weight: (dict) weight for each variable in stratify_on. Defines their amount of
contribution to the optimization score. Uniform weighting by default. Additional
key: "size_diff" defines how test size diff should be weighted.
test_size: (float) test proportion in set(split_on), e.g. 10% of speakers to be held-out
k: (int) number of different splits to be tried out
seed: (int) random seed
Returns:
train_i: (np.array) train set indices in X
test_i: (np.array) test set indices in X
info: (dict) detail information about reference and achieved prob distributions
"size_testset_in_spliton": intended test_size
"size_testset_in_X": optimized test proportion in X
"p_ref_{c}": reference class distribution calculated from stratify_on[c]
"p_test_{c}": test set class distribution calculated from stratify_on[c][test_i]
'''
gss = GroupShuffleSplit(n_splits=k, test_size=test_size,
random_state=seed)
# data size
N = len(y)
# size checks
assert size_check(stratify_on, N), \
f"all stratify_on arrays must have length {N}"
assert size_check(split_on, N), \
f"split_on array must have length {N}"
# set weight defaults
if weight is None:
weight = {}
for c in stratify_on.keys():
if c not in weight:
weight[c] = 1
if "size_diff" not in weight:
weight["size_diff"] = 1
# stratification reference distributions calculated on stratify_on
p_ref = {}
for c in stratify_on:
p_ref[c] = class_prob(stratify_on[c])
# best train and test indices in X; best associated score
train_i, test_i, best_sco = None, None, np.inf
# full target coverage in all partitions
full_target_coverage = False
# categorical target: number of classes for coverage test
if is_categorical(y[0]):
nc = len(set(y))
else:
nc = None
# brute-force optimization of SPLIT_ON split
for tri, tei in gss.split(X, y, split_on):
# all classes maintained in all partitions?
if nc:
nc_train = len(set(y[tri]))
nc_test = len(set(y[tei]))
if min(nc_train, nc_test) < nc:
continue
full_target_coverage = True
sco = calc_split_score(tei, stratify_on, weight, p_ref, N, test_size)
if sco < best_sco:
train_i, test_i, best_sco = tri, tei, sco
if test_i is None:
sys.exit(exit_message(full_target_coverage))
# matching info
info = {"score": best_sco,
"size_testset_in_spliton": test_size,
"size_testset_in_X": np.round(len(test_i) / N, 2)}
for c in p_ref:
info[f"p_{c}_ref"] = p_ref[c]
info[f"p_{c}_test"] = class_prob(stratify_on[c][test_i])
return train_i, test_i, info
| (X: Union[<built-in function array>, pandas.core.frame.DataFrame], y: Union[<built-in function array>, list], split_on: Union[<built-in function array>, list], stratify_on: dict, weight: Optional[dict] = None, test_size: float = 0.1, k: int = 30, seed: int = 42) -> Tuple[<built-in function array>, <built-in function array>, dict] | [
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725,450 | resfo.format | Format |
The format of an res file, either FORMATTED for ascii
or UNFORMATTED for binary.
| class Format(Enum):
"""
The format of an res file, either FORMATTED for ascii
or UNFORMATTED for binary.
"""
FORMATTED = auto()
UNFORMATTED = auto()
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725,451 | resfo.types | MESS |
The MESS value is a sentinell object used to signal that the type of the
array that is to be written should be an empty array of type MESS.
| class MESS:
"""
The MESS value is a sentinell object used to signal that the type of the
array that is to be written should be an empty array of type MESS.
"""
pass
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725,452 | resfo.errors | ResfoParsingError |
Indicates an error occurred during reading of an res file.
| class ResfoParsingError(ValueError):
"""
Indicates an error occurred during reading of an res file.
"""
pass
| null | [
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] |
725,453 | resfo.errors | ResfoWriteError |
Indicates an error occurred during writing of an res file.
| class ResfoWriteError(ValueError):
"""
Indicates an error occurred during writing of an res file.
"""
pass
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] |
725,459 | resfo.read | lazy_read |
Reads the contents of an res file and generates the entries
of that file. Each entry has a entry.read_keyword() and
entry.read_array() method which will return the corresponding
data, but only upon request. This requires the user to
pay attention to when values are read as it should happen
before the file is closed.
When lazy_read is given a path or filename, the file will be closed once
the generator has ran out of elements.
For greater control, one can pass an opened file so that close
can be called at the correct time.
:param filelike: Either filename, pathlib.Path or stream
to write file to. For fileformat=Format.UNFORMATTED the
stream must be in binary mode and for fileformat=Format.FORMATTED
in text mode.
:param fileformat: Either resfo.Format.FORMATTED for ascii
format, resfo.Format.UNFORMATTED for binary formatted files
or None for guess.
:raises resfo.ResfoParsingError: If the file is not a valid
res file.
.. note::
If given a file to be open (as opposed to a stream), the errors
(various `IOError` s) associated with the default behavior of the
built-in `open()` function may be raised.
When given a stream, the exceptions associated with the stream will
pass through.
| def lazy_read(filelike, fileformat: Optional[Format] = None) -> Iterator[ResArray]:
"""
Reads the contents of an res file and generates the entries
of that file. Each entry has a entry.read_keyword() and
entry.read_array() method which will return the corresponding
data, but only upon request. This requires the user to
pay attention to when values are read as it should happen
before the file is closed.
When lazy_read is given a path or filename, the file will be closed once
the generator has ran out of elements.
For greater control, one can pass an opened file so that close
can be called at the correct time.
:param filelike: Either filename, pathlib.Path or stream
to write file to. For fileformat=Format.UNFORMATTED the
stream must be in binary mode and for fileformat=Format.FORMATTED
in text mode.
:param fileformat: Either resfo.Format.FORMATTED for ascii
format, resfo.Format.UNFORMATTED for binary formatted files
or None for guess.
:raises resfo.ResfoParsingError: If the file is not a valid
res file.
.. note::
If given a file to be open (as opposed to a stream), the errors
(various `IOError` s) associated with the default behavior of the
built-in `open()` function may be raised.
When given a stream, the exceptions associated with the stream will
pass through.
"""
if fileformat is None:
fileformat = guess_format(filelike)
stream, didopen = get_stream(filelike, fileformat)
check_correct_mode(stream, fileformat)
try:
if fileformat == Format.FORMATTED:
yield from FormattedArray.parse(stream)
else:
yield from UnformattedResArray.parse(stream)
finally:
if didopen:
stream.close()
| (filelike, fileformat: Optional[resfo.format.Format] = None) -> Iterator[resfo.array_entry.ResArray] | [
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] |
725,460 | resfo.read | read |
Read the contents of a res file and return a list of
tuples (keyword, array). Takes the same parameters as
lazy_read, but differs in return type
| def read(*args, **kwargs) -> List[Tuple[str, "ReadArrayValue"]]:
"""
Read the contents of a res file and return a list of
tuples (keyword, array). Takes the same parameters as
lazy_read, but differs in return type
"""
return [
(arr.read_keyword(), arr.read_array()) for arr in lazy_read(*args, **kwargs)
]
| (*args, **kwargs) -> List[Tuple[str, ForwardRef('ReadArrayValue')]] | [
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] |
725,464 | resfo.write | write |
Write the given contents to the given file in res format.
:param filelike: Either filename, pathlib.Path or stream
to write file to. For fileformat=Format.UNFORMATTED the
stream must be in binary mode and for fileformat=Format.FORMATTED
in text mode.
:param contents: list or iterable of tuples (kw, arr) where keyword
is the keyword, and arr is a numpy arraylike of values. The
keyword must have exactly 8 characters and the type of the array
will be converted according to resfo.types.to_np_type
:param fileformat: Either resfo.Format.FORMATTED for ascii
format or resfo.Format.UNFORMATTED for binary format.
:raises resfo.ResfoWriteError: If the given contents cannot be
written to an res file.
.. note::
If given a file to be open (as opposed to a stream), the errors
(various `IOError` s) associated with the default behavior of the
built-in `open()` function may be raised.
When given a stream, the exceptions associated with the stream will
pass through.
| def write(
filelike,
contents: Union[Sequence[Tuple[str, WriteArrayValue]], Dict[str, WriteArrayValue]],
fileformat: Format = Format.UNFORMATTED,
):
"""
Write the given contents to the given file in res format.
:param filelike: Either filename, pathlib.Path or stream
to write file to. For fileformat=Format.UNFORMATTED the
stream must be in binary mode and for fileformat=Format.FORMATTED
in text mode.
:param contents: list or iterable of tuples (kw, arr) where keyword
is the keyword, and arr is a numpy arraylike of values. The
keyword must have exactly 8 characters and the type of the array
will be converted according to resfo.types.to_np_type
:param fileformat: Either resfo.Format.FORMATTED for ascii
format or resfo.Format.UNFORMATTED for binary format.
:raises resfo.ResfoWriteError: If the given contents cannot be
written to an res file.
.. note::
If given a file to be open (as opposed to a stream), the errors
(various `IOError` s) associated with the default behavior of the
built-in `open()` function may be raised.
When given a stream, the exceptions associated with the stream will
pass through.
"""
stream, didopen = get_stream(filelike, fileformat, mode="w")
check_correct_mode(stream, fileformat)
if fileformat == Format.FORMATTED:
formatted_write(stream, contents)
else:
unformatted_write(stream, contents)
if didopen:
stream.close()
| (filelike, contents: Union[Sequence[Tuple[str, Union[ForwardRef('ArrayLike'), resfo.types.MESS]]], Dict[str, Union[ForwardRef('ArrayLike'), resfo.types.MESS]]], fileformat: resfo.format.Format = <Format.UNFORMATTED: 2>) | [
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] |
725,465 | pydomainextractor | DomainExtractor |
PyDomainExtractor is a highly optimized Domain Name Extraction library written in Rust
| class DomainExtractor:
'''
PyDomainExtractor is a highly optimized Domain Name Extraction library written in Rust
'''
engine: typing.Optional[pydomainextractor.DomainExtractor] = None
def __new__(
cls,
suffix_list_data: typing.Optional[str] = None,
):
if suffix_list_data is None:
if DomainExtractor.engine is None:
DomainExtractor.engine = pydomainextractor.DomainExtractor()
return DomainExtractor.engine
else:
return pydomainextractor.DomainExtractor(suffix_list_data)
| (suffix_list_data: Optional[str] = None) | [
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725,466 | pydomainextractor | __new__ | null | def __new__(
cls,
suffix_list_data: typing.Optional[str] = None,
):
if suffix_list_data is None:
if DomainExtractor.engine is None:
DomainExtractor.engine = pydomainextractor.DomainExtractor()
return DomainExtractor.engine
else:
return pydomainextractor.DomainExtractor(suffix_list_data)
| (cls, suffix_list_data: Optional[str] = None) | [
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725,536 | syne_tune.report | Reporter |
Callback for reporting metric values from a training script back to Syne Tune.
Example:
.. code-block:: python
from syne_tune import Reporter
report = Reporter()
for epoch in range(1, epochs + 1):
# ...
report(epoch=epoch, accuracy=accuracy)
:param add_time: If True (default), the time (in secs) since creation of the
:class:`Reporter` object is reported automatically as
:const:`~syne_tune.constants.ST_WORKER_TIME`
:param add_cost: If True (default), estimated dollar cost since creation of
:class:`Reporter` object is reported automatically as
:const:`~syne_tune.constants.ST_WORKER_COST`. This is available for
SageMaker backend only. Requires ``add_time=True``.
| class Reporter:
"""
Callback for reporting metric values from a training script back to Syne Tune.
Example:
.. code-block:: python
from syne_tune import Reporter
report = Reporter()
for epoch in range(1, epochs + 1):
# ...
report(epoch=epoch, accuracy=accuracy)
:param add_time: If True (default), the time (in secs) since creation of the
:class:`Reporter` object is reported automatically as
:const:`~syne_tune.constants.ST_WORKER_TIME`
:param add_cost: If True (default), estimated dollar cost since creation of
:class:`Reporter` object is reported automatically as
:const:`~syne_tune.constants.ST_WORKER_COST`. This is available for
SageMaker backend only. Requires ``add_time=True``.
"""
add_time: bool = True
add_cost: bool = True
def __post_init__(self):
if self.add_time:
self.start = perf_counter()
self.iter = 0
# TODO dollar-cost computation is not available for file-based backends, what would be
# needed to add support for those backends will be to add a way to access instance-type
# information.
if self.add_cost:
# add instance_type and instance count so that cost can be computed easily
self.instance_type = os.getenv(
f"SM_HP_{ST_INSTANCE_TYPE.upper()}", None
)
self.instance_count = literal_eval(
os.getenv(f"SM_HP_{ST_INSTANCE_COUNT.upper()}", "1")
)
if self.instance_type is not None:
logger.info(
f"detected instance-type/instance-count to {self.instance_type}/{self.instance_count}"
)
instance_infos = InstanceInfos()
if self.instance_type in instance_infos.instances:
cost_per_hour = instance_infos(
instance_type=self.instance_type
).cost_per_hour
self.dollar_cost = cost_per_hour * self.instance_count / 3600
def __call__(self, **kwargs) -> None:
"""Report metric values from training function back to Syne Tune
A time stamp :const:`~syne_tune.constants.ST_WORKER_TIMESTAMP` is added.
See :attr:`add_time`, :attr:`add_cost` comments for other automatically
added metrics.
:param kwargs: Keyword arguments for metrics to be reported, for instance
:code:`report(epoch=1, loss=1.2)`. Values must be serializable with json,
keys should not start with ``st_`` which is a reserved namespace for
Syne Tune internals.
"""
self._check_reported_values(kwargs)
assert not any(key.startswith("st_") for key in kwargs), (
"The metric prefix 'st_' is used by Syne Tune internals, "
"please use a metric name that does not start with 'st_'."
)
kwargs[ST_WORKER_TIMESTAMP] = time()
if self.add_time:
seconds_spent = perf_counter() - self.start
kwargs[ST_WORKER_TIME] = seconds_spent
# second cost will only be there if we were able to properly detect the instance-type and instance-count
# from the environment
if hasattr(self, "dollar_cost"):
kwargs[ST_WORKER_COST] = seconds_spent * self.dollar_cost
kwargs[ST_WORKER_ITER] = self.iter
self.iter += 1
_report_logger(**kwargs)
@staticmethod
def _check_reported_values(kwargs: Dict[str, Any]):
assert all(
v is not None for v in kwargs.values()
), f"Invalid value in report: kwargs = {kwargs}"
| (add_time: bool = True, add_cost: bool = True) -> None | [
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725,537 | syne_tune.report | __call__ | Report metric values from training function back to Syne Tune
A time stamp :const:`~syne_tune.constants.ST_WORKER_TIMESTAMP` is added.
See :attr:`add_time`, :attr:`add_cost` comments for other automatically
added metrics.
:param kwargs: Keyword arguments for metrics to be reported, for instance
:code:`report(epoch=1, loss=1.2)`. Values must be serializable with json,
keys should not start with ``st_`` which is a reserved namespace for
Syne Tune internals.
| def __call__(self, **kwargs) -> None:
"""Report metric values from training function back to Syne Tune
A time stamp :const:`~syne_tune.constants.ST_WORKER_TIMESTAMP` is added.
See :attr:`add_time`, :attr:`add_cost` comments for other automatically
added metrics.
:param kwargs: Keyword arguments for metrics to be reported, for instance
:code:`report(epoch=1, loss=1.2)`. Values must be serializable with json,
keys should not start with ``st_`` which is a reserved namespace for
Syne Tune internals.
"""
self._check_reported_values(kwargs)
assert not any(key.startswith("st_") for key in kwargs), (
"The metric prefix 'st_' is used by Syne Tune internals, "
"please use a metric name that does not start with 'st_'."
)
kwargs[ST_WORKER_TIMESTAMP] = time()
if self.add_time:
seconds_spent = perf_counter() - self.start
kwargs[ST_WORKER_TIME] = seconds_spent
# second cost will only be there if we were able to properly detect the instance-type and instance-count
# from the environment
if hasattr(self, "dollar_cost"):
kwargs[ST_WORKER_COST] = seconds_spent * self.dollar_cost
kwargs[ST_WORKER_ITER] = self.iter
self.iter += 1
_report_logger(**kwargs)
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725,538 | syne_tune.report | __eq__ | null | # Copyright 2021 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file is distributed
# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied. See the License for the specific language governing
# permissions and limitations under the License.
import os
import re
import sys
import json
import logging
from ast import literal_eval
from typing import List, Dict, Any
from time import time, perf_counter
from dataclasses import dataclass
from syne_tune.constants import (
ST_INSTANCE_TYPE,
ST_INSTANCE_COUNT,
ST_WORKER_TIME,
ST_WORKER_COST,
ST_WORKER_TIMESTAMP,
ST_WORKER_ITER,
ST_SAGEMAKER_METRIC_TAG,
)
from syne_tune.util import dump_json_with_numpy
# this is required so that metrics are written
from syne_tune.backend.sagemaker_backend.instance_info import InstanceInfos
logging.basicConfig()
logger = logging.getLogger(__name__)
@dataclass
class Reporter:
"""
Callback for reporting metric values from a training script back to Syne Tune.
Example:
.. code-block:: python
from syne_tune import Reporter
report = Reporter()
for epoch in range(1, epochs + 1):
# ...
report(epoch=epoch, accuracy=accuracy)
:param add_time: If True (default), the time (in secs) since creation of the
:class:`Reporter` object is reported automatically as
:const:`~syne_tune.constants.ST_WORKER_TIME`
:param add_cost: If True (default), estimated dollar cost since creation of
:class:`Reporter` object is reported automatically as
:const:`~syne_tune.constants.ST_WORKER_COST`. This is available for
SageMaker backend only. Requires ``add_time=True``.
"""
add_time: bool = True
add_cost: bool = True
def __post_init__(self):
if self.add_time:
self.start = perf_counter()
self.iter = 0
# TODO dollar-cost computation is not available for file-based backends, what would be
# needed to add support for those backends will be to add a way to access instance-type
# information.
if self.add_cost:
# add instance_type and instance count so that cost can be computed easily
self.instance_type = os.getenv(
f"SM_HP_{ST_INSTANCE_TYPE.upper()}", None
)
self.instance_count = literal_eval(
os.getenv(f"SM_HP_{ST_INSTANCE_COUNT.upper()}", "1")
)
if self.instance_type is not None:
logger.info(
f"detected instance-type/instance-count to {self.instance_type}/{self.instance_count}"
)
instance_infos = InstanceInfos()
if self.instance_type in instance_infos.instances:
cost_per_hour = instance_infos(
instance_type=self.instance_type
).cost_per_hour
self.dollar_cost = cost_per_hour * self.instance_count / 3600
def __call__(self, **kwargs) -> None:
"""Report metric values from training function back to Syne Tune
A time stamp :const:`~syne_tune.constants.ST_WORKER_TIMESTAMP` is added.
See :attr:`add_time`, :attr:`add_cost` comments for other automatically
added metrics.
:param kwargs: Keyword arguments for metrics to be reported, for instance
:code:`report(epoch=1, loss=1.2)`. Values must be serializable with json,
keys should not start with ``st_`` which is a reserved namespace for
Syne Tune internals.
"""
self._check_reported_values(kwargs)
assert not any(key.startswith("st_") for key in kwargs), (
"The metric prefix 'st_' is used by Syne Tune internals, "
"please use a metric name that does not start with 'st_'."
)
kwargs[ST_WORKER_TIMESTAMP] = time()
if self.add_time:
seconds_spent = perf_counter() - self.start
kwargs[ST_WORKER_TIME] = seconds_spent
# second cost will only be there if we were able to properly detect the instance-type and instance-count
# from the environment
if hasattr(self, "dollar_cost"):
kwargs[ST_WORKER_COST] = seconds_spent * self.dollar_cost
kwargs[ST_WORKER_ITER] = self.iter
self.iter += 1
_report_logger(**kwargs)
@staticmethod
def _check_reported_values(kwargs: Dict[str, Any]):
assert all(
v is not None for v in kwargs.values()
), f"Invalid value in report: kwargs = {kwargs}"
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725,540 | syne_tune.report | __post_init__ | null | def __post_init__(self):
if self.add_time:
self.start = perf_counter()
self.iter = 0
# TODO dollar-cost computation is not available for file-based backends, what would be
# needed to add support for those backends will be to add a way to access instance-type
# information.
if self.add_cost:
# add instance_type and instance count so that cost can be computed easily
self.instance_type = os.getenv(
f"SM_HP_{ST_INSTANCE_TYPE.upper()}", None
)
self.instance_count = literal_eval(
os.getenv(f"SM_HP_{ST_INSTANCE_COUNT.upper()}", "1")
)
if self.instance_type is not None:
logger.info(
f"detected instance-type/instance-count to {self.instance_type}/{self.instance_count}"
)
instance_infos = InstanceInfos()
if self.instance_type in instance_infos.instances:
cost_per_hour = instance_infos(
instance_type=self.instance_type
).cost_per_hour
self.dollar_cost = cost_per_hour * self.instance_count / 3600
| (self) | [
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] |
725,542 | syne_tune.report | _check_reported_values | null | @staticmethod
def _check_reported_values(kwargs: Dict[str, Any]):
assert all(
v is not None for v in kwargs.values()
), f"Invalid value in report: kwargs = {kwargs}"
| (kwargs: Dict[str, Any]) | [
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725,543 | syne_tune.stopping_criterion | StoppingCriterion |
Stopping criterion that can be used in a Tuner, for instance
:code:`Tuner(stop_criterion=StoppingCriterion(max_wallclock_time=3600), ...)`.
If several arguments are used, the combined criterion is true whenever
one of the atomic criteria is true.
In principle, ``stop_criterion`` for ``Tuner`` can be any lambda function, but
this class should be used with remote launching in order to ensure
proper serialization.
:param max_wallclock_time: Stop once this wallclock time is reached
:param max_num_evaluations: Stop once more than this number of metric
records have been reported
:param max_num_trials_started: Stop once more than this number of trials
have been started
:param max_num_trials_completed: Stop once more than this number of trials
have been completed. This does not include trials which were stopped
or failed
:param max_cost: Stop once total cost of evaluations larger than this value
:param max_num_trials_finished: Stop once more than this number of trials
have finished (i.e., completed, stopped, failed, or stopping)
:param min_metric_value: Dictionary with thresholds for selected metrics.
Stop once an evaluation reports a metric value below a threshold
:param max_metric_value: Dictionary with thresholds for selected metrics.
Stop once an evaluation reports a metric value above a threshold
| class StoppingCriterion:
"""
Stopping criterion that can be used in a Tuner, for instance
:code:`Tuner(stop_criterion=StoppingCriterion(max_wallclock_time=3600), ...)`.
If several arguments are used, the combined criterion is true whenever
one of the atomic criteria is true.
In principle, ``stop_criterion`` for ``Tuner`` can be any lambda function, but
this class should be used with remote launching in order to ensure
proper serialization.
:param max_wallclock_time: Stop once this wallclock time is reached
:param max_num_evaluations: Stop once more than this number of metric
records have been reported
:param max_num_trials_started: Stop once more than this number of trials
have been started
:param max_num_trials_completed: Stop once more than this number of trials
have been completed. This does not include trials which were stopped
or failed
:param max_cost: Stop once total cost of evaluations larger than this value
:param max_num_trials_finished: Stop once more than this number of trials
have finished (i.e., completed, stopped, failed, or stopping)
:param min_metric_value: Dictionary with thresholds for selected metrics.
Stop once an evaluation reports a metric value below a threshold
:param max_metric_value: Dictionary with thresholds for selected metrics.
Stop once an evaluation reports a metric value above a threshold
"""
max_wallclock_time: float = None
max_num_evaluations: int = None
max_num_trials_started: int = None
max_num_trials_completed: int = None
max_cost: float = None
max_num_trials_finished: int = None
# minimum value for metrics, any value below this threshold will trigger a stop
min_metric_value: Optional[Dict[str, float]] = None
# maximum value for metrics, any value above this threshold will trigger a stop
max_metric_value: Optional[Dict[str, float]] = None
# todo we should have unit-test for all those cases.
def __call__(self, status: TuningStatus) -> bool:
if (
self.max_wallclock_time is not None
and status.wallclock_time > self.max_wallclock_time
):
logger.info(
f"reaching max wallclock time ({self.max_wallclock_time}), stopping there."
)
return True
if (
self.max_num_trials_started is not None
and status.num_trials_started > self.max_num_trials_started
):
logger.info(
f"reaching max number of trials started ({self.max_num_trials_started + 1}), stopping there."
)
return True
if (
self.max_num_trials_completed is not None
and status.num_trials_completed > self.max_num_trials_completed
):
logger.info(
f"reaching max number of trials completed ({self.max_num_trials_completed + 1}), stopping there."
)
return True
if (
self.max_num_trials_finished is not None
and status.num_trials_finished > self.max_num_trials_finished
):
logger.info(
f"reaching max number of trials finished ({self.max_num_trials_finished + 1}), stopping there."
)
return True
if self.max_cost is not None and status.cost > self.max_cost:
logger.info(f"reaching max cost ({self.max_cost}), stopping there.")
return True
if (
self.max_num_evaluations is not None
and status.overall_metric_statistics.count > self.max_num_evaluations
):
logger.info(
f"reaching {status.overall_metric_statistics.count + 1} evaluations, stopping there. "
)
return True
if (
self.max_metric_value is not None
and status.overall_metric_statistics.count > 0
):
max_metrics_observed = status.overall_metric_statistics.max_metrics
for metric, max_metric_accepted in self.max_metric_value.items():
if (
metric in max_metrics_observed
and max_metrics_observed[metric] > max_metric_accepted
):
logger.info(
f"found {metric} with value ({max_metrics_observed[metric]}), "
f"above the provided threshold {max_metric_accepted} stopping there."
)
return True
if (
self.min_metric_value is not None
and status.overall_metric_statistics.count > 0
):
min_metrics_observed = status.overall_metric_statistics.min_metrics
for metric, min_metric_accepted in self.min_metric_value.items():
if (
metric in min_metrics_observed
and min_metrics_observed[metric] < min_metric_accepted
):
logger.info(
f"found {metric} with value ({min_metrics_observed[metric]}), "
f"below the provided threshold {min_metric_accepted} stopping there."
)
return True
return False
| (max_wallclock_time: float = None, max_num_evaluations: int = None, max_num_trials_started: int = None, max_num_trials_completed: int = None, max_cost: float = None, max_num_trials_finished: int = None, min_metric_value: Optional[Dict[str, float]] = None, max_metric_value: Optional[Dict[str, float]] = None) -> None | [
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725,544 | syne_tune.stopping_criterion | __call__ | null | def __call__(self, status: TuningStatus) -> bool:
if (
self.max_wallclock_time is not None
and status.wallclock_time > self.max_wallclock_time
):
logger.info(
f"reaching max wallclock time ({self.max_wallclock_time}), stopping there."
)
return True
if (
self.max_num_trials_started is not None
and status.num_trials_started > self.max_num_trials_started
):
logger.info(
f"reaching max number of trials started ({self.max_num_trials_started + 1}), stopping there."
)
return True
if (
self.max_num_trials_completed is not None
and status.num_trials_completed > self.max_num_trials_completed
):
logger.info(
f"reaching max number of trials completed ({self.max_num_trials_completed + 1}), stopping there."
)
return True
if (
self.max_num_trials_finished is not None
and status.num_trials_finished > self.max_num_trials_finished
):
logger.info(
f"reaching max number of trials finished ({self.max_num_trials_finished + 1}), stopping there."
)
return True
if self.max_cost is not None and status.cost > self.max_cost:
logger.info(f"reaching max cost ({self.max_cost}), stopping there.")
return True
if (
self.max_num_evaluations is not None
and status.overall_metric_statistics.count > self.max_num_evaluations
):
logger.info(
f"reaching {status.overall_metric_statistics.count + 1} evaluations, stopping there. "
)
return True
if (
self.max_metric_value is not None
and status.overall_metric_statistics.count > 0
):
max_metrics_observed = status.overall_metric_statistics.max_metrics
for metric, max_metric_accepted in self.max_metric_value.items():
if (
metric in max_metrics_observed
and max_metrics_observed[metric] > max_metric_accepted
):
logger.info(
f"found {metric} with value ({max_metrics_observed[metric]}), "
f"above the provided threshold {max_metric_accepted} stopping there."
)
return True
if (
self.min_metric_value is not None
and status.overall_metric_statistics.count > 0
):
min_metrics_observed = status.overall_metric_statistics.min_metrics
for metric, min_metric_accepted in self.min_metric_value.items():
if (
metric in min_metrics_observed
and min_metrics_observed[metric] < min_metric_accepted
):
logger.info(
f"found {metric} with value ({min_metrics_observed[metric]}), "
f"below the provided threshold {min_metric_accepted} stopping there."
)
return True
return False
| (self, status: syne_tune.tuning_status.TuningStatus) -> bool | [
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725,545 | syne_tune.stopping_criterion | __eq__ | null | # Copyright 2021 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file is distributed
# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied. See the License for the specific language governing
# permissions and limitations under the License.
import logging
import numpy as np
from dataclasses import dataclass
from typing import Optional, Dict
from syne_tune.tuning_status import TuningStatus
logger = logging.getLogger(__name__)
@dataclass
class StoppingCriterion:
"""
Stopping criterion that can be used in a Tuner, for instance
:code:`Tuner(stop_criterion=StoppingCriterion(max_wallclock_time=3600), ...)`.
If several arguments are used, the combined criterion is true whenever
one of the atomic criteria is true.
In principle, ``stop_criterion`` for ``Tuner`` can be any lambda function, but
this class should be used with remote launching in order to ensure
proper serialization.
:param max_wallclock_time: Stop once this wallclock time is reached
:param max_num_evaluations: Stop once more than this number of metric
records have been reported
:param max_num_trials_started: Stop once more than this number of trials
have been started
:param max_num_trials_completed: Stop once more than this number of trials
have been completed. This does not include trials which were stopped
or failed
:param max_cost: Stop once total cost of evaluations larger than this value
:param max_num_trials_finished: Stop once more than this number of trials
have finished (i.e., completed, stopped, failed, or stopping)
:param min_metric_value: Dictionary with thresholds for selected metrics.
Stop once an evaluation reports a metric value below a threshold
:param max_metric_value: Dictionary with thresholds for selected metrics.
Stop once an evaluation reports a metric value above a threshold
"""
max_wallclock_time: float = None
max_num_evaluations: int = None
max_num_trials_started: int = None
max_num_trials_completed: int = None
max_cost: float = None
max_num_trials_finished: int = None
# minimum value for metrics, any value below this threshold will trigger a stop
min_metric_value: Optional[Dict[str, float]] = None
# maximum value for metrics, any value above this threshold will trigger a stop
max_metric_value: Optional[Dict[str, float]] = None
# todo we should have unit-test for all those cases.
def __call__(self, status: TuningStatus) -> bool:
if (
self.max_wallclock_time is not None
and status.wallclock_time > self.max_wallclock_time
):
logger.info(
f"reaching max wallclock time ({self.max_wallclock_time}), stopping there."
)
return True
if (
self.max_num_trials_started is not None
and status.num_trials_started > self.max_num_trials_started
):
logger.info(
f"reaching max number of trials started ({self.max_num_trials_started + 1}), stopping there."
)
return True
if (
self.max_num_trials_completed is not None
and status.num_trials_completed > self.max_num_trials_completed
):
logger.info(
f"reaching max number of trials completed ({self.max_num_trials_completed + 1}), stopping there."
)
return True
if (
self.max_num_trials_finished is not None
and status.num_trials_finished > self.max_num_trials_finished
):
logger.info(
f"reaching max number of trials finished ({self.max_num_trials_finished + 1}), stopping there."
)
return True
if self.max_cost is not None and status.cost > self.max_cost:
logger.info(f"reaching max cost ({self.max_cost}), stopping there.")
return True
if (
self.max_num_evaluations is not None
and status.overall_metric_statistics.count > self.max_num_evaluations
):
logger.info(
f"reaching {status.overall_metric_statistics.count + 1} evaluations, stopping there. "
)
return True
if (
self.max_metric_value is not None
and status.overall_metric_statistics.count > 0
):
max_metrics_observed = status.overall_metric_statistics.max_metrics
for metric, max_metric_accepted in self.max_metric_value.items():
if (
metric in max_metrics_observed
and max_metrics_observed[metric] > max_metric_accepted
):
logger.info(
f"found {metric} with value ({max_metrics_observed[metric]}), "
f"above the provided threshold {max_metric_accepted} stopping there."
)
return True
if (
self.min_metric_value is not None
and status.overall_metric_statistics.count > 0
):
min_metrics_observed = status.overall_metric_statistics.min_metrics
for metric, min_metric_accepted in self.min_metric_value.items():
if (
metric in min_metrics_observed
and min_metrics_observed[metric] < min_metric_accepted
):
logger.info(
f"found {metric} with value ({min_metrics_observed[metric]}), "
f"below the provided threshold {min_metric_accepted} stopping there."
)
return True
return False
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725,548 | syne_tune.tuner | Tuner |
Controller of tuning loop, manages interplay between scheduler and
trial backend. Also, stopping criterion and number of workers are
maintained here.
:param trial_backend: Backend for trial evaluations
:param scheduler: Tuning algorithm for making decisions about which
trials to start, stop, pause, or resume
:param stop_criterion: Tuning stops when this predicates returns ``True``.
Called in each iteration with the current tuning status. It is
recommended to use :class:`StoppingCriterion`.
:param n_workers: Number of workers used here. Note that the backend
needs to support (at least) this number of workers to be run
in parallel
:param sleep_time: Time to sleep when all workers are busy. Defaults to
:const:`~syne_tune.constants.DEFAULT_SLEEP_TIME`
:param results_update_interval: Frequency at which results are updated and
stored (in seconds). Defaults to 10.
:param print_update_interval: Frequency at which result table is printed.
Defaults to 30.
:param max_failures: This many trial execution failures are allowed before
the tuning loop is aborted. Defaults to 1
:param tuner_name: Name associated with the tuning experiment, default to
the name of the entrypoint. Must consists of alpha-digits characters,
possibly separated by '-'. A postfix with a date time-stamp is added
to ensure uniqueness.
:param asynchronous_scheduling: Whether to use asynchronous scheduling
when scheduling new trials. If ``True``, trials are scheduled as soon as
a worker is available. If ``False``, the tuner waits that all trials
are finished before scheduling a new batch of size ``n_workers``.
Default to ``True``.
:param wait_trial_completion_when_stopping: How to deal with running
trials when stopping criterion is met. If ``True``, the tuner waits
until all trials are finished. If ``False``, all trials are terminated.
Defaults to ``False``.
:param callbacks: Called at certain times in the tuning loop, for example
when a result is seen. The default callback stores results every
``results_update_interval``.
:param metadata: Dictionary of user-metadata that will be persisted in
``{tuner_path}/{ST_METADATA_FILENAME}``, in addition to metadata provided by
the user. ``SMT_TUNER_CREATION_TIMESTAMP`` is always included which
measures the time-stamp when the tuner started to run.
:param suffix_tuner_name: If ``True``, a timestamp is appended to the
provided ``tuner_name`` that ensures uniqueness, otherwise the name is
left unchanged and is expected to be unique. Defaults to ``True``.
:param save_tuner: If ``True``, the :class:`Tuner` object is serialized at
the end of tuning, including its dependencies (e.g., scheduler). This
allows all details of the experiment to be recovered. Defaults to
``True``.
:param start_jobs_without_delay: Defaults to ``True``. If this is ``True``, the tuner
starts new jobs depending on scheduler decisions communicated to the
backend. For example, if a trial has just been stopped (by calling
``backend.stop_trial``), the tuner may start a new one immediately, even
if the SageMaker training job is still busy due to stopping delays.
This can lead to faster experiment runtime, because the backend is
temporarily going over its budget.
If set to ``False``, the tuner always asks the backend for the number of
busy workers, which guarantees that we never go over the ``n_workers``
budget. This makes a difference for backends where stopping or pausing
trials is not immediate (e.g., :class:`SageMakerBackend`). Not going
over budget means that ``n_workers`` can be set up to the available quota,
without running the risk of an exception due to the quota being
exceeded. If you get such exceptions, we recommend to use
``start_jobs_without_delay=False``. Also, if the SageMaker warm pool
feature is used, it is recommended to set
``start_jobs_without_delay=False``, since otherwise more than ``n_workers``
warm pools will be started, because existing ones are busy with
stopping when they should be reassigned.
:param trial_backend_path: If this is given, the path of ``trial_backend``
(where logs and checkpoints of trials are stored) is set to this.
Otherwise, it is set to ``self.tuner_path``, so that per-trial
information is written to the same path as tuning results.
If the backend is :class:`~syne_tune.backend.LocalBackend` and the
experiment is ru remotely, we recommend to set this, since otherwise
checkpoints and logs are synced to S3, along with tuning results, which
is costly and error-prone.
| class Tuner:
"""
Controller of tuning loop, manages interplay between scheduler and
trial backend. Also, stopping criterion and number of workers are
maintained here.
:param trial_backend: Backend for trial evaluations
:param scheduler: Tuning algorithm for making decisions about which
trials to start, stop, pause, or resume
:param stop_criterion: Tuning stops when this predicates returns ``True``.
Called in each iteration with the current tuning status. It is
recommended to use :class:`StoppingCriterion`.
:param n_workers: Number of workers used here. Note that the backend
needs to support (at least) this number of workers to be run
in parallel
:param sleep_time: Time to sleep when all workers are busy. Defaults to
:const:`~syne_tune.constants.DEFAULT_SLEEP_TIME`
:param results_update_interval: Frequency at which results are updated and
stored (in seconds). Defaults to 10.
:param print_update_interval: Frequency at which result table is printed.
Defaults to 30.
:param max_failures: This many trial execution failures are allowed before
the tuning loop is aborted. Defaults to 1
:param tuner_name: Name associated with the tuning experiment, default to
the name of the entrypoint. Must consists of alpha-digits characters,
possibly separated by '-'. A postfix with a date time-stamp is added
to ensure uniqueness.
:param asynchronous_scheduling: Whether to use asynchronous scheduling
when scheduling new trials. If ``True``, trials are scheduled as soon as
a worker is available. If ``False``, the tuner waits that all trials
are finished before scheduling a new batch of size ``n_workers``.
Default to ``True``.
:param wait_trial_completion_when_stopping: How to deal with running
trials when stopping criterion is met. If ``True``, the tuner waits
until all trials are finished. If ``False``, all trials are terminated.
Defaults to ``False``.
:param callbacks: Called at certain times in the tuning loop, for example
when a result is seen. The default callback stores results every
``results_update_interval``.
:param metadata: Dictionary of user-metadata that will be persisted in
``{tuner_path}/{ST_METADATA_FILENAME}``, in addition to metadata provided by
the user. ``SMT_TUNER_CREATION_TIMESTAMP`` is always included which
measures the time-stamp when the tuner started to run.
:param suffix_tuner_name: If ``True``, a timestamp is appended to the
provided ``tuner_name`` that ensures uniqueness, otherwise the name is
left unchanged and is expected to be unique. Defaults to ``True``.
:param save_tuner: If ``True``, the :class:`Tuner` object is serialized at
the end of tuning, including its dependencies (e.g., scheduler). This
allows all details of the experiment to be recovered. Defaults to
``True``.
:param start_jobs_without_delay: Defaults to ``True``. If this is ``True``, the tuner
starts new jobs depending on scheduler decisions communicated to the
backend. For example, if a trial has just been stopped (by calling
``backend.stop_trial``), the tuner may start a new one immediately, even
if the SageMaker training job is still busy due to stopping delays.
This can lead to faster experiment runtime, because the backend is
temporarily going over its budget.
If set to ``False``, the tuner always asks the backend for the number of
busy workers, which guarantees that we never go over the ``n_workers``
budget. This makes a difference for backends where stopping or pausing
trials is not immediate (e.g., :class:`SageMakerBackend`). Not going
over budget means that ``n_workers`` can be set up to the available quota,
without running the risk of an exception due to the quota being
exceeded. If you get such exceptions, we recommend to use
``start_jobs_without_delay=False``. Also, if the SageMaker warm pool
feature is used, it is recommended to set
``start_jobs_without_delay=False``, since otherwise more than ``n_workers``
warm pools will be started, because existing ones are busy with
stopping when they should be reassigned.
:param trial_backend_path: If this is given, the path of ``trial_backend``
(where logs and checkpoints of trials are stored) is set to this.
Otherwise, it is set to ``self.tuner_path``, so that per-trial
information is written to the same path as tuning results.
If the backend is :class:`~syne_tune.backend.LocalBackend` and the
experiment is ru remotely, we recommend to set this, since otherwise
checkpoints and logs are synced to S3, along with tuning results, which
is costly and error-prone.
"""
def __init__(
self,
trial_backend: TrialBackend,
scheduler: TrialScheduler,
stop_criterion: Callable[[TuningStatus], bool],
n_workers: int,
sleep_time: float = TUNER_DEFAULT_SLEEP_TIME,
results_update_interval: float = 10.0,
print_update_interval: float = 30.0,
max_failures: int = 1,
tuner_name: Optional[str] = None,
asynchronous_scheduling: bool = True,
wait_trial_completion_when_stopping: bool = False,
callbacks: Optional[List[TunerCallback]] = None,
metadata: Optional[dict] = None,
suffix_tuner_name: bool = True,
save_tuner: bool = True,
start_jobs_without_delay: bool = True,
trial_backend_path: Optional[str] = None,
):
self.trial_backend = trial_backend
self.scheduler = scheduler
self.n_workers = n_workers
self.sleep_time = sleep_time
self.results_update_interval = results_update_interval
self.stop_criterion = stop_criterion
self.asynchronous_scheduling = asynchronous_scheduling
self.wait_trial_completion_when_stopping = wait_trial_completion_when_stopping
self.metadata = self._enrich_metadata(metadata)
self.save_tuner = save_tuner
self.start_jobs_without_delay = start_jobs_without_delay
self.max_failures = max_failures
self.print_update_interval = print_update_interval
if tuner_name is not None:
check_valid_sagemaker_name(tuner_name)
else:
tuner_name = Path(self.trial_backend.entrypoint_path()).stem.replace(
"_", "-"
)
if suffix_tuner_name or tuner_name is None:
self.name = name_from_base(tuner_name, default="st-tuner")
else:
self.name = tuner_name
# we keep track of the last result seen to send it to schedulers when trials complete.
self.last_seen_result_per_trial = {}
self.trials_scheduler_stopped = set()
self.tuner_path = Path(experiment_path(tuner_name=self.name))
# inform the backend to the folder of the Tuner. This allows the local backend
# to store the logs and tuner results in the same folder.
self.trial_backend.set_path(
results_root=str(self.tuner_path)
if trial_backend_path is None
else trial_backend_path,
tuner_name=self.name,
)
self._init_callbacks(callbacks)
self.tuning_status = None
self.tuner_saver = None
self.status_printer = None
self._initialize_early_checkpoint_removal()
def _init_callbacks(self, callbacks: Optional[List[TunerCallback]]):
if callbacks is None:
callbacks = [self._default_callback()]
else:
if not any(
isinstance(callback, StoreResultsCallback) for callback in callbacks
):
logger.warning(
"None of the callbacks provided are of type StoreResultsCallback. "
"This means no tuning results will be written."
)
self.callbacks: List[TunerCallback] = | (trial_backend: syne_tune.backend.trial_backend.TrialBackend, scheduler: syne_tune.optimizer.scheduler.TrialScheduler, stop_criterion: Callable[[syne_tune.tuning_status.TuningStatus], bool], n_workers: int, sleep_time: float = 5.0, results_update_interval: float = 10.0, print_update_interval: float = 30.0, max_failures: int = 1, tuner_name: Optional[str] = None, asynchronous_scheduling: bool = True, wait_trial_completion_when_stopping: bool = False, callbacks: Optional[List[syne_tune.tuner_callback.TunerCallback]] = None, metadata: Optional[dict] = None, suffix_tuner_name: bool = True, save_tuner: bool = True, start_jobs_without_delay: bool = True, trial_backend_path: Optional[str] = None) | [
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725,549 | syne_tune.tuner | __init__ | null | def __init__(
self,
trial_backend: TrialBackend,
scheduler: TrialScheduler,
stop_criterion: Callable[[TuningStatus], bool],
n_workers: int,
sleep_time: float = TUNER_DEFAULT_SLEEP_TIME,
results_update_interval: float = 10.0,
print_update_interval: float = 30.0,
max_failures: int = 1,
tuner_name: Optional[str] = None,
asynchronous_scheduling: bool = True,
wait_trial_completion_when_stopping: bool = False,
callbacks: Optional[List[TunerCallback]] = None,
metadata: Optional[dict] = None,
suffix_tuner_name: bool = True,
save_tuner: bool = True,
start_jobs_without_delay: bool = True,
trial_backend_path: Optional[str] = None,
):
self.trial_backend = trial_backend
self.scheduler = scheduler
self.n_workers = n_workers
self.sleep_time = sleep_time
self.results_update_interval = results_update_interval
self.stop_criterion = stop_criterion
self.asynchronous_scheduling = asynchronous_scheduling
self.wait_trial_completion_when_stopping = wait_trial_completion_when_stopping
self.metadata = self._enrich_metadata(metadata)
self.save_tuner = save_tuner
self.start_jobs_without_delay = start_jobs_without_delay
self.max_failures = max_failures
self.print_update_interval = print_update_interval
if tuner_name is not None:
check_valid_sagemaker_name(tuner_name)
else:
tuner_name = Path(self.trial_backend.entrypoint_path()).stem.replace(
"_", "-"
)
if suffix_tuner_name or tuner_name is None:
self.name = name_from_base(tuner_name, default="st-tuner")
else:
self.name = tuner_name
# we keep track of the last result seen to send it to schedulers when trials complete.
self.last_seen_result_per_trial = {}
self.trials_scheduler_stopped = set()
self.tuner_path = Path(experiment_path(tuner_name=self.name))
# inform the backend to the folder of the Tuner. This allows the local backend
# to store the logs and tuner results in the same folder.
self.trial_backend.set_path(
results_root=str(self.tuner_path)
if trial_backend_path is None
else trial_backend_path,
tuner_name=self.name,
)
self._init_callbacks(callbacks)
self.tuning_status = None
self.tuner_saver = None
self.status_printer = None
self._initialize_early_checkpoint_removal()
| (self, trial_backend: syne_tune.backend.trial_backend.TrialBackend, scheduler: syne_tune.optimizer.scheduler.TrialScheduler, stop_criterion: Callable[[syne_tune.tuning_status.TuningStatus], bool], n_workers: int, sleep_time: float = 5.0, results_update_interval: float = 10.0, print_update_interval: float = 30.0, max_failures: int = 1, tuner_name: Optional[str] = None, asynchronous_scheduling: bool = True, wait_trial_completion_when_stopping: bool = False, callbacks: Optional[List[syne_tune.tuner_callback.TunerCallback]] = None, metadata: Optional[dict] = None, suffix_tuner_name: bool = True, save_tuner: bool = True, start_jobs_without_delay: bool = True, trial_backend_path: Optional[str] = None) | [
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725,550 | syne_tune.tuner | _default_callback |
:return: Default callback to store results
| @staticmethod
def _default_callback():
"""
:return: Default callback to store results
"""
return StoreResultsCallback()
| () | [
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725,551 | syne_tune.tuner | _enrich_metadata |
:param metadata: Original metadata
:return: ``metadata`` enriched by default entries
| def _enrich_metadata(self, metadata: Dict[str, Any]) -> Dict[str, Any]:
"""
:param metadata: Original metadata
:return: ``metadata`` enriched by default entries
"""
res = metadata if metadata is not None else dict()
self._set_metadata(res, ST_TUNER_CREATION_TIMESTAMP, time.time())
self._set_metadata(res, "entrypoint", self.trial_backend.entrypoint_path().stem)
self._set_metadata(res, "backend", str(type(self.trial_backend).__name__))
for key, value in self.scheduler.metadata().items():
self._set_metadata(res, key, value)
return res
| (self, metadata: Dict[str, Any]) -> Dict[str, Any] | [
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725,552 | syne_tune.tuner | _handle_failure | null | def _handle_failure(self, done_trials_statuses: Dict[int, Tuple[Trial, str]]):
logger.error(f"Stopped as {self.max_failures} failures were reached")
for trial_id, (_, status) in done_trials_statuses.items():
if status == Status.failed:
logger.error(f"showing log of first failure")
stdout = "".join(self.trial_backend.stdout(trial_id))
stderr = "".join(self.trial_backend.stderr(trial_id))
logger.error(stdout)
logger.error(stderr)
raise ValueError(f"Trial - {trial_id} failed")
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