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Edit docstrings (#1013)
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pollfly
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@ -470,7 +470,7 @@ class PipelineController(object):
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pass
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:param post_execute_callback: Callback function, called when a step (Task) is completed
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and it other jobs are executed. Allows a user to modify the Task status after completion.
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and other jobs are executed. Allows a user to modify the Task status after completion.
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.. code-block:: py
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@ -738,7 +738,7 @@ class PipelineController(object):
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pass
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:param post_execute_callback: Callback function, called when a step (Task) is completed
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and it other jobs are executed. Allows a user to modify the Task status after completion.
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and other jobs are executed. Allows a user to modify the Task status after completion.
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.. code-block:: py
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@ -862,7 +862,7 @@ class PipelineController(object):
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pass
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:param Callable step_task_completed_callback: Callback function, called when a step (Task) is completed
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and it other jobs are executed. Allows a user to modify the Task status after completion.
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and other jobs are executed. Allows a user to modify the Task status after completion.
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.. code-block:: py
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@ -951,7 +951,7 @@ class PipelineController(object):
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def connect_configuration(self, configuration, name=None, description=None):
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# type: (Union[Mapping, list, Path, str], Optional[str], Optional[str]) -> Union[dict, Path, str]
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"""
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Connect a configuration dictionary or configuration file (pathlib.Path / str) to a the PipelineController object.
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Connect a configuration dictionary or configuration file (pathlib.Path / str) to the PipelineController object.
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This method should be called before reading the configuration file.
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For example, a local file:
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@ -1373,7 +1373,7 @@ class PipelineController(object):
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pass
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:param Callable step_task_completed_callback: Callback function, called when a step (Task) is completed
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and it other jobs are executed. Allows a user to modify the Task status after completion.
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and other jobs are executed. Allows a user to modify the Task status after completion.
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.. code-block:: py
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@ -1895,7 +1895,7 @@ class PipelineController(object):
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pass
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:param post_execute_callback: Callback function, called when a step (Task) is completed
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and it other jobs are executed. Allows a user to modify the Task status after completion.
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and other jobs are executed. Allows a user to modify the Task status after completion.
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.. code-block:: py
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@ -3644,7 +3644,7 @@ class PipelineDecorator(PipelineController):
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pass
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:param post_execute_callback: Callback function, called when a step (Task) is completed
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and it other jobs are executed. Allows a user to modify the Task status after completion.
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and other jobs are executed. Allows a user to modify the Task status after completion.
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.. code-block:: py
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@ -229,14 +229,15 @@ class OptimizerBOHB(SearchStrategy, RandomSeed):
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year = {2018},
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}
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:param eta : float (3)
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:param eta: float (3)
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In each iteration, a complete run of sequential halving is executed. In it,
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after evaluating each configuration on the same subset size, only a fraction of
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1/eta of them 'advances' to the next round.
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Must be greater or equal to 2.
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:param min_budget : float (0.01)
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:param min_budget: float (0.01)
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The smallest budget to consider. Needs to be positive!
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:param max_budget : float (1)
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:param max_budget: float (1)
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The largest budget to consider. Needs to be larger than min_budget!
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The budgets will be geometrically distributed
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:math:`a^2 + b^2 = c^2 /sim /eta^k` for :math:`k/in [0, 1, ... , num/_subsets - 1]`.
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@ -432,7 +432,7 @@ class SearchStrategy(object):
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Helper function, Implementation is not required. Default use in process_step default implementation.
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Check if the job needs to be aborted or already completed.
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If returns ``False``, the job was aborted / completed, and should be taken off the current job list
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If returns ``False``, the job was aborted / completed, and should be taken off the current job list.
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If there is a budget limitation, this call should update
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``self.budget.compute_time.update`` / ``self.budget.iterations.update``
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@ -534,6 +534,8 @@ class SearchStrategy(object):
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where index 0 is the best performing Task.
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Example w/ all_metrics=False:
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.. code-block:: py
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[
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('0593b76dc7234c65a13a301f731958fa',
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{
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@ -550,6 +552,8 @@ class SearchStrategy(object):
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Example w/ all_metrics=True:
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.. code-block:: py
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[
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('0593b76dc7234c65a13a301f731958fa',
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{
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@ -599,9 +603,8 @@ class SearchStrategy(object):
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# type: (int, bool, bool, bool) -> Sequence[(str, dict)]
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"""
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Return a list of dictionaries of the top performing experiments.
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Example: [
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{'task_id': Task-ID, 'metrics': scalar-metric-dict, 'hyper_parameters': Hyper-Parameters},
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]
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Example: ``[{'task_id': Task-ID, 'metrics': scalar-metric-dict, 'hyper_parameters': Hyper-Parameters},]``
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Order is based on the controller ``Objective`` object.
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:param int top_k: The number of Tasks (experiments) to return.
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@ -614,46 +617,50 @@ class SearchStrategy(object):
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where index 0 is the best performing Task.
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Example w/ all_metrics=False:
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[
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{
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task_id: '0593b76dc7234c65a13a301f731958fa',
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hyper_parameters: {'General/lr': '0.03', 'General/batch_size': '32'},
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metrics: {
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'accuracy per class/cat': {
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'metric': 'accuracy per class',
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'variant': 'cat',
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'value': 0.119,
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'min_value': 0.119,
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'max_value': 0.782
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},
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}
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},
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]
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.. code-block:: py
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Example w/ all_metrics=True:
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[
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{
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task_id: '0593b76dc7234c65a13a301f731958fa',
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hyper_parameters: {'General/lr': '0.03', 'General/batch_size': '32'},
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metrics: {
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'accuracy per class/cat': {
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'metric': 'accuracy per class',
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'variant': 'cat',
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'value': 0.119,
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'min_value': 0.119,
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'max_value': 0.782
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},
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}
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},
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]
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[
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{
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task_id: '0593b76dc7234c65a13a301f731958fa',
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hyper_parameters: {'General/lr': '0.03', 'General/batch_size': '32'},
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metrics: {
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'accuracy per class/cat': {
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'metric': 'accuracy per class',
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'variant': 'cat',
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'value': 0.119,
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'min_value': 0.119,
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'max_value': 0.782
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},
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'accuracy per class/deer': {
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'metric': 'accuracy per class',
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'variant': 'deer',
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'value': 0.219,
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'min_value': 0.219,
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'max_value': 0.282
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},
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}
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},
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]
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Example w/ all_metrics=True:
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.. code-block:: py
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[
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{
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task_id: '0593b76dc7234c65a13a301f731958fa',
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hyper_parameters: {'General/lr': '0.03', 'General/batch_size': '32'},
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metrics: {
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'accuracy per class/cat': {
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'metric': 'accuracy per class',
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'variant': 'cat',
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'value': 0.119,
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'min_value': 0.119,
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'max_value': 0.782
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},
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'accuracy per class/deer': {
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'metric': 'accuracy per class',
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'variant': 'deer',
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'value': 0.219,
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'min_value': 0.219,
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'max_value': 0.282
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},
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}
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},
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]
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"""
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additional_filters = dict(page_size=int(top_k), page=0)
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if only_completed:
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@ -761,7 +768,8 @@ class SearchStrategy(object):
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"""
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Set the function used to name a newly created job.
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:param callable naming_function:
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:param callable naming_function: Callable function for naming a newly created job.
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Use the following format:
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.. code-block:: py
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@ -1072,7 +1080,7 @@ class RandomSearch(SearchStrategy):
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class HyperParameterOptimizer(object):
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"""
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Hyper-parameter search controller. Clones the base experiment, changes arguments and tries to maximize/minimize
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Hyperparameter search controller. Clones the base experiment, changes arguments and tries to maximize/minimize
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the defined objective.
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"""
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_tag = 'optimization'
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@ -1105,13 +1113,12 @@ class HyperParameterOptimizer(object):
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``validation``).
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:param str objective_metric_series: The Objective metric series to maximize / minimize (for example, ``loss``).
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:param str objective_metric_sign: The objective to maximize / minimize.
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The values are:
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- ``min`` - Minimize the last reported value for the specified title/series scalar.
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- ``max`` - Maximize the last reported value for the specified title/series scalar.
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- ``min_global`` - Minimize the min value of *all* reported values for the specific title/series scalar.
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- ``max_global`` - Maximize the max value of *all* reported values for the specific title/series scalar.
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- ``min`` - Minimize the last reported value for the specified title/series scalar.
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- ``max`` - Maximize the last reported value for the specified title/series scalar.
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- ``min_global`` - Minimize the min value of *all* reported values for the specific title/series scalar.
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- ``max_global`` - Maximize the max value of *all* reported values for the specific title/series scalar.
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:param class.SearchStrategy optimizer_class: The SearchStrategy optimizer to use for the hyper-parameter search
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:param int max_number_of_concurrent_tasks: The maximum number of concurrent Tasks (experiments) running at the
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@ -1121,24 +1128,21 @@ class HyperParameterOptimizer(object):
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default is ``None``, indicating no time limit.
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:param float compute_time_limit: The maximum compute time in minutes. When time limit is exceeded,
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all jobs aborted. (Optional)
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:param bool auto_connect_task: Store optimization arguments and configuration in the Task
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:param bool auto_connect_task: Store optimization arguments and configuration in the Task.
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The values are:
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- ``True`` - The optimization argument and configuration will be stored in the Task. All arguments will
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be under the hyper-parameter section ``opt``, and the optimization hyper_parameters space will
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- ``True`` - The optimization argument and configuration will be stored in the Task. All arguments will
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be under the hyperparameter section ``opt``, and the optimization hyper_parameters space will be
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stored in the Task configuration object section.
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- ``False`` - Do not store with Task.
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- ``Task`` - A specific Task object to connect the optimization process with.
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- ``False`` - Do not store with Task.
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- ``Task`` - A specific Task object to connect the optimization process with.
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:param bool always_create_task: Always create a new Task
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:param bool always_create_task: Always create a new Task.
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The values are:
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- ``True`` - No current Task initialized. Create a new task named ``optimization`` in the ``base_task_id``
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- ``True`` - No current Task initialized. Create a new task named ``optimization`` in the ``base_task_id``
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project.
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- ``False`` - Use the :py:meth:`task.Task.current_task` (if exists) to report statistics.
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- ``False`` - Use the :py:meth:`task.Task.current_task` (if exists) to report statistics.
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:param str spawn_project: If project name is specified, create all optimization Jobs (Tasks) in the
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specified project instead of the original base_task_id project.
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@ -1505,9 +1509,8 @@ class HyperParameterOptimizer(object):
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# type: (int, bool, bool, bool) -> Sequence[(str, dict)]
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"""
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Return a list of dictionaries of the top performing experiments.
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Example: [
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{'task_id': Task-ID, 'metrics': scalar-metric-dict, 'hyper_parameters': Hyper-Parameters},
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]
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Example: ``[{'task_id': Task-ID, 'metrics': scalar-metric-dict, 'hyper_parameters': Hyper-Parameters},]``
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Order is based on the controller ``Objective`` object.
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:param int top_k: The number of Tasks (experiments) to return.
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@ -1520,46 +1523,50 @@ class HyperParameterOptimizer(object):
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where index 0 is the best performing Task.
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Example w/ all_metrics=False:
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[
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{
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task_id: '0593b76dc7234c65a13a301f731958fa',
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hyper_parameters: {'General/lr': '0.03', 'General/batch_size': '32'},
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metrics: {
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'accuracy per class/cat': {
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'metric': 'accuracy per class',
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'variant': 'cat',
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'value': 0.119,
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'min_value': 0.119,
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'max_value': 0.782
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},
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}
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},
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]
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.. code-block:: py
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Example w/ all_metrics=True:
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[
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{
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task_id: '0593b76dc7234c65a13a301f731958fa',
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hyper_parameters: {'General/lr': '0.03', 'General/batch_size': '32'},
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metrics: {
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'accuracy per class/cat': {
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'metric': 'accuracy per class',
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'variant': 'cat',
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'value': 0.119,
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'min_value': 0.119,
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'max_value': 0.782
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},
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}
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},
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]
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[
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{
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task_id: '0593b76dc7234c65a13a301f731958fa',
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hyper_parameters: {'General/lr': '0.03', 'General/batch_size': '32'},
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metrics: {
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'accuracy per class/cat': {
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'metric': 'accuracy per class',
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'variant': 'cat',
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'value': 0.119,
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'min_value': 0.119,
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'max_value': 0.782
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},
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'accuracy per class/deer': {
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'metric': 'accuracy per class',
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'variant': 'deer',
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'value': 0.219,
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'min_value': 0.219,
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'max_value': 0.282
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},
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}
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},
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]
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Example w/ all_metrics=True:
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.. code-block:: py
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[
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{
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task_id: '0593b76dc7234c65a13a301f731958fa',
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hyper_parameters: {'General/lr': '0.03', 'General/batch_size': '32'},
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metrics: {
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'accuracy per class/cat': {
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'metric': 'accuracy per class',
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'variant': 'cat',
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'value': 0.119,
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'min_value': 0.119,
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'max_value': 0.782
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},
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'accuracy per class/deer': {
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'metric': 'accuracy per class',
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'variant': 'deer',
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'value': 0.219,
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'min_value': 0.219,
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'max_value': 0.282
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},
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}
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},
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]
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"""
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if not self.optimizer:
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return []
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@ -1615,13 +1622,12 @@ class HyperParameterOptimizer(object):
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``validation``).
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:param str objective_metric_series: The Objective metric series to maximize / minimize (for example, ``loss``).
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:param str objective_metric_sign: The objective to maximize / minimize.
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The values are:
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- ``min`` - Minimize the last reported value for the specified title/series scalar.
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- ``max`` - Maximize the last reported value for the specified title/series scalar.
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- ``min_global`` - Minimize the min value of *all* reported values for the specific title/series scalar.
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- ``max_global`` - Maximize the max value of *all* reported values for the specific title/series scalar.
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- ``min`` - Minimize the last reported value for the specified title/series scalar.
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- ``max`` - Maximize the last reported value for the specified title/series scalar.
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- ``min_global`` - Minimize the min value of *all* reported values for the specific title/series scalar.
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- ``max_global`` - Maximize the max value of *all* reported values for the specific title/series scalar.
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:param str optimizer_task_id: Parent optimizer Task ID
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:param top_k: The number of Tasks (experiments) to return.
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:return: A list of Task objects, ordered by performance, where index 0 is the best performing Task.
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@ -110,7 +110,7 @@ class Parameter(RandomSeed):
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class UniformParameterRange(Parameter):
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"""
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Uniform randomly sampled hyper-parameter object.
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Uniform randomly sampled hyperparameter object.
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"""
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def __init__(
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@ -129,12 +129,11 @@ class UniformParameterRange(Parameter):
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:param float min_value: The minimum sample to use for uniform random sampling.
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:param float max_value: The maximum sample to use for uniform random sampling.
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:param float step_size: If not ``None``, set step size (quantization) for value sampling.
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:param bool include_max_value: Range includes the ``max_value``
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:param bool include_max_value: Range includes the ``max_value``.
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The values are:
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- ``True`` - The range includes the ``max_value`` (Default)
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- ``False`` - Does not include.
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- ``True`` - The range includes the ``max_value`` (Default)
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- ``False`` - Does not include.
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"""
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super(UniformParameterRange, self).__init__(name=name)
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@ -221,7 +220,7 @@ class LogUniformParameterRange(UniformParameterRange):
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class UniformIntegerParameterRange(Parameter):
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"""
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Uniform randomly sampled integer Hyper-Parameter object.
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Uniform randomly sampled integer Hyperparameter object.
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"""
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def __init__(self, name, min_value, max_value, step_size=1, include_max_value=True):
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@ -233,12 +232,11 @@ class UniformIntegerParameterRange(Parameter):
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:param int min_value: The minimum sample to use for uniform random sampling.
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:param int max_value: The maximum sample to use for uniform random sampling.
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:param int step_size: The default step size is ``1``.
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:param bool include_max_value: Range includes the ``max_value``
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:param bool include_max_value: Range includes the ``max_value``.
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The values are:
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- ``True`` - Includes the ``max_value`` (Default)
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- ``False`` - Does not include.
|
||||
- ``True`` - Includes the ``max_value`` (Default)
|
||||
- ``False`` - Does not include.
|
||||
|
||||
"""
|
||||
super(UniformIntegerParameterRange, self).__init__(name=name)
|
||||
|
||||
@ -324,6 +324,7 @@ class Dataset(object):
|
||||
# type: () -> Mapping[str, LinkEntry]
|
||||
"""
|
||||
Notice this call returns an internal representation, do not modify!
|
||||
|
||||
:return: dict with relative file path as key, and LinkEntry as value
|
||||
"""
|
||||
return self._dataset_link_entries
|
||||
@ -643,8 +644,9 @@ class Dataset(object):
|
||||
If -1 is provided, use a single zip artifact for the entire dataset change-set (old behaviour)
|
||||
:param max_workers: Numbers of threads to be spawned when zipping and uploading the files.
|
||||
If None (default) it will be set to:
|
||||
- 1: if the upload destination is a cloud provider ('s3', 'gs', 'azure')
|
||||
- number of logical cores: otherwise
|
||||
|
||||
- 1: if the upload destination is a cloud provider ('s3', 'gs', 'azure')
|
||||
- number of logical cores: otherwise
|
||||
:param int retries: Number of retries before failing to upload each zip. If 0, the upload is not retried.
|
||||
|
||||
:raise: If the upload failed (i.e. at least one zip failed to upload), raise a `ValueError`
|
||||
@ -839,7 +841,7 @@ class Dataset(object):
|
||||
# type: (Union[numpy.array, pd.DataFrame, Dict[str, Any]], str, bool) -> () # noqa: F821
|
||||
"""
|
||||
Attach a user-defined metadata to the dataset. Check `Task.upload_artifact` for supported types.
|
||||
If type is Optionally make it visible as a table in the UI.
|
||||
If type is Pandas Dataframes, optionally make it visible as a table in the UI.
|
||||
"""
|
||||
if metadata_name.startswith(self.__data_entry_name_prefix):
|
||||
raise ValueError("metadata_name can not start with '{}'".format(self.__data_entry_name_prefix))
|
||||
@ -954,7 +956,7 @@ class Dataset(object):
|
||||
# type: (Union[Path, _Path, str], bool, Optional[int], Optional[int], bool, Optional[int]) -> Optional[str]
|
||||
"""
|
||||
return a base folder with a writable (mutable) local copy of the entire dataset
|
||||
download and copy / soft-link, files from all the parent dataset versions
|
||||
download and copy / soft-link, files from all the parent dataset versions
|
||||
|
||||
:param target_folder: Target folder for the writable copy
|
||||
:param overwrite: If True, recursively delete the target folder before creating a copy.
|
||||
@ -1223,11 +1225,11 @@ class Dataset(object):
|
||||
:param output_uri: Location to upload the datasets file to, including preview samples.
|
||||
The following are examples of ``output_uri`` values for the supported locations:
|
||||
|
||||
- A shared folder: ``/mnt/share/folder``
|
||||
- S3: ``s3://bucket/folder``
|
||||
- Google Cloud Storage: ``gs://bucket-name/folder``
|
||||
- Azure Storage: ``azure://company.blob.core.windows.net/folder/``
|
||||
- Default file server: None
|
||||
- A shared folder: ``/mnt/share/folder``
|
||||
- S3: ``s3://bucket/folder``
|
||||
- Google Cloud Storage: ``gs://bucket-name/folder``
|
||||
- Azure Storage: ``azure://company.blob.core.windows.net/folder/``
|
||||
- Default file server: None
|
||||
|
||||
:param description: Description of the dataset
|
||||
|
||||
@ -1786,6 +1788,7 @@ class Dataset(object):
|
||||
"""
|
||||
Return a Logger object for the Dataset, allowing users to report statistics metrics
|
||||
and debug samples on the Dataset itself
|
||||
|
||||
:return: Logger object
|
||||
"""
|
||||
return self._task.get_logger()
|
||||
@ -1797,8 +1800,8 @@ class Dataset(object):
|
||||
(it does not imply on the number of chunks parent versions store)
|
||||
|
||||
:param include_parents: If True (default),
|
||||
return the total number of chunks from this version and all parent versions.
|
||||
If False, only return the number of chunks we stored on this specific version.
|
||||
return the total number of chunks from this version and all parent versions.
|
||||
If False, only return the number of chunks we stored on this specific version.
|
||||
|
||||
:return: Number of chunks stored on the dataset.
|
||||
"""
|
||||
|
||||
Loading…
Reference in New Issue
Block a user