callbacks

import tensorwaves.optimizer.callbacks

Collection of loggers that can be inserted into an optimizer as callback.

class Loadable

Bases: ABC

abstractmethod static load_latest_parameters(filename: Path | str) → dict

class Callback

Bases: ABC

Interface for callbacks such as CSVSummary.

See also

Callbacks

abstractmethod on_optimize_start(logs: dict[str, Any] | None = None) → None

abstractmethod on_optimize_end(logs: dict[str, Any] | None = None) → None

abstractmethod on_iteration_end(iteration: int, logs: dict[str, Any] | None = None) → None

abstractmethod on_function_call_end(function_call: int, logs: dict[str, Any] | None = None) → None

class CallbackList(callbacks: Iterable[Callback])

Bases: Callback

Class for combining Callback s.

Combine different Callback classes in to a chain as follows:

>>> from tensorwaves.optimizer import Minuit2
>>> optimizer = Minuit2(
...     callback=CallbackList([TFSummary(), YAMLSummary("fit_result.yml")])
... )

property callbacks: list[Callback]

on_optimize_start(logs: dict[str, Any] | None = None) → None

on_optimize_end(logs: dict[str, Any] | None = None) → None

on_iteration_end(iteration: int, logs: dict[str, Any] | None = None) → None

on_function_call_end(function_call: int, logs: dict[str, Any] | None = None) → None

class RichProgressBar(*columns: str | ProgressColumn, total: int | None = None, **progress_kwargs: Any)

Bases: Callback

Display a rich progress bar during optimization.

Parameters:

• *columns – The columns to display in the progress bar. If not provided, a default set of columns will be used.

• **progress_kwargs – Keyword arguments forwarded to rich.progress.Progress.

• total – The expected total number of function calls to be made during optimization in order to get a time estimate.

on_optimize_start(logs: dict[str, Any] | None = None) → None

on_optimize_end(logs: dict[str, Any] | None = None) → None

on_iteration_end(iteration: int, logs: dict[str, Any] | None = None) → None

on_function_call_end(function_call: int, logs: dict[str, Any] | None = None) → None

class TqdmProgressBar(**tqdm_kwargs: Any)

Bases: Callback

Display a tqdm progress bar during optimization.

Parameters:

**tqdm_kwargs – Keyword arguments forwarded to tqdm.

on_optimize_start(logs: dict[str, Any] | None = None) → None

on_optimize_end(logs: dict[str, Any] | None = None) → None

on_iteration_end(iteration: int, logs: dict[str, Any] | None = None) → None

on_function_call_end(function_call: int, logs: dict[str, Any] | None = None) → None

class CSVSummary(filename: Path | str, function_call_step_size: int = 1, iteration_step_size: int = 1)

Bases: Callback, Loadable

Log fit parameters and the estimator value to a CSV file.

on_optimize_start(logs: dict[str, Any] | None = None) → None

on_optimize_end(logs: dict[str, Any] | None = None) → None

on_iteration_end(iteration: int, logs: dict[str, Any] | None = None) → None

on_function_call_end(function_call: int, logs: dict[str, Any] | None = None) → None

static load_latest_parameters(filename: Path | str) → dict

class TFSummary(logdir: str = 'logs', step_size: int = 10, subdir: str | None = None)

Bases: Callback

Log fit parameters and the estimator value to a tf.summary.

The logs can be viewed with TensorBoard via:

tensorboard --logdir logs

on_optimize_start(logs: dict[str, Any] | None = None) → None

on_optimize_end(logs: dict[str, Any] | None = None) → None

on_iteration_end(iteration: int, logs: dict[str, Any] | None = None) → None

on_function_call_end(function_call: int, logs: dict[str, Any] | None = None) → None

class YAMLSummary(filename: Path | str, step_size: int = 10, git_friendly: bool = False)

Bases: Callback, Loadable

Write current fit parameters and the estimator value to a YAML file.

Parameters:

• filename – The name of output YAML file to write the logs to.

• step_size – The number of function calls between each log entry.

• git_friendly – If True, entries that are differ per run in reproducible fits, such as time, are omitted from the log.

on_optimize_start(logs: dict[str, Any] | None = None) → None

on_optimize_end(logs: dict[str, Any] | None = None) → None

on_iteration_end(iteration: int, logs: dict[str, Any] | None = None) → None

on_function_call_end(function_call: int, logs: dict[str, Any] | None = None) → None

static load_latest_parameters(filename: Path | str) → dict