from typing import Any, Literal, Tuple, Union
import pandas as pd
from ._multi_case_timer import ResultsDict
[docs]def to_dataframe(run_results: ResultsDict) -> pd.DataFrame:
"""Create a DataFrame from performance run output, adding derived values.
Args:
run_results: Output from :meth:`rics.performance.MultiCaseTimer.run`.
Returns:
The `run_result` input wrapped in a DataFrame.
"""
ans = []
for candidate_label, candidate_results in run_results.items():
for data_label, data_results in candidate_results.items():
ans.append(
pd.DataFrame(
{
"Candidate": candidate_label,
"Test data": data_label,
"Run no": range(len(data_results)),
"Time [s]": data_results,
}
)
)
df = pd.concat(ans, ignore_index=True)
df["Time [ms]"] = df["Time [s]"] * 1000
df["Time [μs]"] = df["Time [ms]"] * 1000
groupby = df.groupby("Test data")["Time [s]"]
df["Times min"] = df["Time [s]"] / df["Test data"].map(groupby.min())
df["Times mean"] = df["Time [s]"] / df["Test data"].map(groupby.mean())
return df
[docs]def get_best(run_results: Union[ResultsDict, pd.DataFrame], per_candidate: bool = False) -> pd.DataFrame:
"""Get a summarized view of the best run results for each candidate/data pair.
Args:
run_results: Output of :meth:`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.
"""
df = run_results if isinstance(run_results, pd.DataFrame) else to_dataframe(run_results)
return df.sort_values("Time [s]").groupby(["Candidate", "Test data"] if per_candidate else "Test data").head(1)
[docs]def plot_run(
run_results: Union[ResultsDict, pd.DataFrame],
x: Literal["candidate", "data"] = None,
unit: Literal["s", "ms", "μs", "us"] = "ms",
**figure_kwargs: Any,
) -> None:
"""Plot the results of a performance test.
.. figure:: ../_images/perf_plot.png
Comparison of ``time.sleep(t)`` and ``time.sleep(5*t)``.
Args:
run_results: Output of :meth:`rics.performance.MultiCaseTimer.run`.
x: The value to plot on the X-axis. Default=derive.
unit: Time unit to plot on the Y-axis.
**figure_kwargs: Keyword arguments for the barplot.
Raises:
ModuleNotFoundError: If Seaborn isn't installed.
TypeError: For unknown `unit` arguments.
"""
import matplotlib.pyplot as plt
from seaborn import barplot, move_legend
data = to_dataframe(run_results) if isinstance(run_results, dict) else run_results.copy()
data[["Test data", "Candidate"]] = data[["Test data", "Candidate"]].astype("category")
x_arg, hue = (
_smaller_as_hue(data)
if x is None
else (("Test data", "Candidate") if x == "data" else ("Candidate", "Test data"))
)
y = f"Time [{unit.replace('us', 'μs')}]"
if y not in data:
# Unit is not one of the literals, but we still check 'data' in case someone added more units themselves.
raise TypeError(f"Bad {unit=}; column '{y}' not present in data.")
fig, (left, right) = plt.subplots(
ncols=2, tight_layout=True, figsize=(8 + 4 * data.Candidate.nunique(), 7), sharey=True
)
left.set_title("Average")
right.set_title("Best")
fig.suptitle("Performance", size=24)
barplot(ax=left, data=data, x=x_arg, y=y, hue=hue, errorbar="sd", **figure_kwargs)
best = data.groupby(["Test data", "Candidate"]).min().reset_index()
barplot(ax=right, data=best, x=x_arg, y=y, hue=hue, errorbar=None, **figure_kwargs)
move_legend(right, "upper left", bbox_to_anchor=(1, 1))
left.get_legend().remove()
def _smaller_as_hue(data: pd.DataFrame) -> Tuple[str, str]:
unique = data.nunique()
return ("Test data", "Candidate") if unique["Test data"] < unique["Candidate"] else ("Candidate", "Test data")