rics.performance

Performance testing utility.

Functions

format_perf_counter(start[, end])	Deprecated alias of rics.strings.format_perf_counter().
format_seconds(t, *[, allow_negative])	Deprecated alias of rics.strings.format_seconds().
get_best(run_results[, per_candidate])	Get a summarized view of the best run results for each candidate/data pair.
plot_run(run_results[, x, unit])	Plot the results of a performance test.
run_multivariate_test(candidate_method, ...)	Run performance tests for multiple candidate methods on collections of test data.
to_dataframe(run_results[, names])	Create a DataFrame from performance run output, adding derived values.

Classes

MultiCaseTimer(candidate_method, test_data)

Performance testing implementation for multiple candidates and data sets.

class MultiCaseTimer(candidate_method: dict[str, Callable[[DataType], Any]] | Collection[Callable[[DataType], Any]] | Callable[[DataType], Any], test_data: dict[Hashable, DataType] | Collection[DataType])

Bases: Generic[DataType]

Performance testing implementation for multiple candidates and data sets.

For non-dict inputs, string labels will be generated automatically.

Parameters:

• candidate_method – A dict {label: function}. Alternatively, you may pass a collection of functions or a single function.

• test_data – A {label: data} to evaluate candidates on. Alternatively, you may pass a list of data.

run(time_per_candidate: float = 6.0, repeat: int = 5, number: int | None = None, progress: bool = False) → dict[str, dict[Hashable, list[float]]]

Run for all cases.

Note that the test case variant data isn’t included in the expected runtime computation, so increasing the amount of test data variants (at initialization) will reduce the amount of times each candidate is evaluated.

Parameters:

• time_per_candidate – Desired runtime for each repetition per candidate label. Ignored if number is set.

• repeat – Number of times to repeat for all candidates per data label.

• number – Number of times to execute each test case, per repetition. Compute based on per_case_time_allocation if None.

• progress – If True, display a progress bar. Required tqdm.

Examples

If repeat=5 and time_per_candidate=3 for an instance with and 2 candidates, the total runtime will be approximately 5 * 3 * 2 = 30 seconds.

Returns:

A dict run_results on the form {candidate_label: {data_label: [runtime, ...]}}.

Raises:

ValueError – If the total expected runtime exceeds max_expected_runtime.

Notes

• Precomputed runtime is inaccurate for functions where a single call are longer than time_per_candidate.

See also

The timeit.Timer class which this implementation depends on.

format_perf_counter(start: float, end: float | None = None) → str

Deprecated alias of rics.strings.format_perf_counter().

format_seconds(t: float, *, allow_negative: bool = False) → str

Deprecated alias of rics.strings.format_seconds().

get_best(run_results: dict[str, dict[Hashable, list[float]]] | DataFrame, per_candidate: bool = False) → DataFrame

Get a summarized view of the best run results for each candidate/data pair.

Parameters:

• run_results – Output of rics.performance.MultiCaseTimer.run().

• per_candidate – If True, show the best times for all candidate/data pairs. Otherwise, just show the best candidate per data label.

Returns:

The best (lowest) times for each candidate/data pair.

plot_run(run_results: dict[str, dict[Hashable, list[float]]] | DataFrame, x: Literal['candidate', 'data'] | None = None, unit: Literal['s', 'ms', 'μs', 'us', 'ns'] | None = None, **kwargs: Any) → None

Plot the results of a performance test.

Comparison of time.sleep(t) and time.sleep(5*t).

Parameters:

• run_results – Output of rics.performance.MultiCaseTimer.run().

• x – The value to plot on the X-axis. Default=derive.

• unit – Time unit to plot on the Y-axis. Default=derive.

• **kwargs – Keyword arguments for seaborn.barplot().

Raises:

• ModuleNotFoundError – If Seaborn isn’t installed.

• TypeError – For unknown unit arguments.

run_multivariate_test(candidate_method: dict[str, Callable[[DataType], Any]] | Collection[Callable[[DataType], Any]] | Callable[[DataType], Any], test_data: dict[Hashable, DataType] | Collection[DataType], time_per_candidate: float = 6.0, plot: bool = True, **figure_kwargs: Any) → DataFrame

Run performance tests for multiple candidate methods on collections of test data.

This is a convenience method which combines MultiCaseTimer.run(), to_dataframe() and – if plotting is enabled – plot_run(). For full functionally these methods should be use directly.

Parameters:

• candidate_method – A single method, collection of functions or a dict {label: function} of candidates.

• test_data – A single datum, or a dict {label: data} to evaluate candidates on.

• time_per_candidate – Desired runtime for each repetition per candidate label.

• plot – If True, plot a figure using plot_run().

• **figure_kwargs – Keyword arguments for the :seaborn.barplot`. Ignored if plot=False.

Returns:

A long-format DataFrame of results.

Raises:

ModuleNotFoundError – If Seaborn isn’t installed and plot=True.

See also

The plot_run() and get_best() functions.

to_dataframe(run_results: dict[str, dict[Hashable, list[float]]], names: Iterable[str] = ()) → DataFrame

Create a DataFrame from performance run output, adding derived values.

Parameters:

• run_results – Output from rics.performance.MultiCaseTimer.run().

• run_results – A dict run_results on the form {candidate_label: {data_label: [runtime, ...]}}, returned by rics.performance.MultiCaseTimer.run().

• names – Categories for keys in the data. If given, all data labels must be tuples of the same length as names.

Returns:

The run_result input wrapped in a DataFrame.

Modules

rics.performance.cli	Multivariate performance testing from the command line.
rics.performance.plot	Plotting backend for the performance framework.
rics.performance.types	Types used by the framework.