"""Plot a set of `Progress` or `StatRun` objects into one figure."""
from math import isfinite
from typing import Any, Callable, Final, Iterable
from matplotlib.artist import Artist # type: ignore
from matplotlib.axes import Axes # type: ignore
from matplotlib.figure import Figure # type: ignore
from pycommons.types import type_error
import moptipy.utils.plot_defaults as pd
import moptipy.utils.plot_utils as pu
from moptipy.evaluation.axis_ranger import AxisRanger
from moptipy.evaluation.base import get_algorithm, get_instance, sort_key
from moptipy.evaluation.progress import Progress
from moptipy.evaluation.stat_run import StatRun, get_statistic
from moptipy.evaluation.styler import Styler
from moptipy.utils.lang import Lang
[docs]
def plot_progress(
progresses: Iterable[Progress | StatRun],
figure: Axes | Figure,
x_axis: AxisRanger | Callable[[str], AxisRanger] =
AxisRanger.for_axis,
y_axis: AxisRanger | Callable[[str], AxisRanger] =
AxisRanger.for_axis,
legend: bool = True,
distinct_colors_func: Callable[[int], Any] = pd.distinct_colors,
distinct_line_dashes_func: Callable[[int], Any] =
pd.distinct_line_dashes,
importance_to_line_width_func: Callable[[int], float] =
pd.importance_to_line_width,
importance_to_alpha_func: Callable[[int], float] =
pd.importance_to_alpha,
importance_to_font_size_func: Callable[[int], float] =
pd.importance_to_font_size,
x_grid: bool = True,
y_grid: bool = True,
x_label: None | str | Callable[[str], str] = Lang.translate,
x_label_inside: bool = True,
x_label_location: float = 0.5,
y_label: None | str | Callable[[str], str] = Lang.translate,
y_label_inside: bool = True,
y_label_location: float = 1.0,
instance_priority: float = 0.666,
algorithm_priority: float = 0.333,
stat_priority: float = 0.0,
instance_sort_key: Callable[[str], Any] = lambda x: x,
algorithm_sort_key: Callable[[str], Any] = lambda x: x,
stat_sort_key: Callable[[str], Any] = lambda x: x,
color_algorithms_as_fallback_group: bool = True,
instance_namer: Callable[[str], str] = lambda x: x,
algorithm_namer: Callable[[str], str] = lambda x: x) -> Axes:
"""
Plot a set of progress or statistical run lines into one chart.
:param progresses: the iterable of progresses and statistical runs
:param figure: the figure to plot in
:param x_axis: the x_axis ranger
:param y_axis: the y_axis ranger
:param legend: should we plot the legend?
:param distinct_colors_func: the function returning the palette
:param distinct_line_dashes_func: the function returning the line styles
:param importance_to_line_width_func: the function converting importance
values to line widths
:param importance_to_alpha_func: the function converting importance
values to alphas
:param importance_to_font_size_func: the function converting importance
values to font sizes
:param x_grid: should we have a grid along the x-axis?
:param y_grid: should we have a grid along the y-axis?
:param x_label: a callable returning the label for the x-axis, a label
string, or `None` if no label should be put
:param x_label_inside: put the x-axis label inside the plot (so that
it does not consume additional vertical space)
:param x_label_location: the location of the x-axis label
:param y_label: a callable returning the label for the y-axis, a label
string, or `None` if no label should be put
:param y_label_inside: put the y-axis label inside the plot (so that
it does not consume additional horizontal space)
:param y_label_location: the location of the y-axis label
:param instance_priority: the style priority for instances
:param algorithm_priority: the style priority for algorithms
:param stat_priority: the style priority for statistics
:param instance_sort_key: the sort key function for instances
:param algorithm_sort_key: the sort key function for algorithms
:param stat_sort_key: the sort key function for statistics
:param color_algorithms_as_fallback_group: if only a single group of data
was found, use algorithms as group and put them in the legend
:param instance_namer: the name function for instances receives an
instance ID and returns an instance name; default=identity function
:param algorithm_namer: the name function for algorithms receives an
algorithm ID and returns an algorithm name; default=identity function
:returns: the axes object to allow you to add further plot elements
"""
# Before doing anything, let's do some type checking on the parameters.
# I want to ensure that this function is called correctly before we begin
# to actually process the data. It is better to fail early than to deliver
# some incorrect results.
if not isinstance(progresses, Iterable):
raise type_error(progresses, "progresses", Iterable)
if not isinstance(figure, Axes | Figure):
raise type_error(figure, "figure", (Axes, Figure))
if not isinstance(legend, bool):
raise type_error(legend, "legend", bool)
if not callable(distinct_colors_func):
raise type_error(
distinct_colors_func, "distinct_colors_func", call=True)
if not callable(distinct_colors_func):
raise type_error(
distinct_colors_func, "distinct_colors_func", call=True)
if not callable(distinct_line_dashes_func):
raise type_error(
distinct_line_dashes_func, "distinct_line_dashes_func", call=True)
if not callable(importance_to_line_width_func):
raise type_error(importance_to_line_width_func,
"importance_to_line_width_func", call=True)
if not callable(importance_to_alpha_func):
raise type_error(
importance_to_alpha_func, "importance_to_alpha_func", call=True)
if not callable(importance_to_font_size_func):
raise type_error(importance_to_font_size_func,
"importance_to_font_size_func", call=True)
if not isinstance(x_grid, bool):
raise type_error(x_grid, "x_grid", bool)
if not isinstance(y_grid, bool):
raise type_error(y_grid, "y_grid", bool)
if not ((x_label is None) or callable(x_label)
or isinstance(x_label, str)):
raise type_error(x_label, "x_label", (str, None), call=True)
if not isinstance(x_label_inside, bool):
raise type_error(x_label_inside, "x_label_inside", bool)
if not isinstance(x_label_location, float):
raise type_error(x_label_location, "x_label_location", float)
if not ((y_label is None) or callable(y_label)
or isinstance(y_label, str)):
raise type_error(y_label, "y_label", (str, None), call=True)
if not isinstance(y_label_inside, bool):
raise type_error(y_label_inside, "y_label_inside", bool)
if not isinstance(y_label_location, float):
raise type_error(y_label_location, "y_label_location", float)
if not isinstance(instance_priority, float):
raise type_error(instance_priority, "instance_priority", float)
if not isfinite(instance_priority):
raise ValueError(f"instance_priority cannot be {instance_priority}.")
if not isinstance(algorithm_priority, float):
raise type_error(algorithm_priority, "algorithm_priority", float)
if not isfinite(algorithm_priority):
raise ValueError(f"algorithm_priority cannot be {algorithm_priority}.")
if not isinstance(stat_priority, float):
raise type_error(stat_priority, "stat_priority", float)
if not isfinite(stat_priority):
raise ValueError(f"stat_priority cannot be {stat_priority}.")
if not callable(instance_sort_key):
raise type_error(instance_sort_key, "instance_sort_key", call=True)
if not callable(algorithm_sort_key):
raise type_error(algorithm_sort_key, "algorithm_sort_key", call=True)
if not callable(stat_sort_key):
raise type_error(stat_sort_key, "stat_sort_key", call=True)
if not callable(instance_namer):
raise type_error(instance_namer, "instance_namer", call=True)
if not callable(algorithm_namer):
raise type_error(algorithm_namer, "algorithm_namer", call=True)
if not isinstance(color_algorithms_as_fallback_group, bool):
raise type_error(color_algorithms_as_fallback_group,
"color_algorithms_as_fallback_group", bool)
# First, we try to find groups of data to plot together in the same
# color/style. We distinguish progress objects from statistical runs.
instances: Final[Styler] = Styler(key_func=get_instance,
namer=instance_namer,
none_name=Lang.translate("all_insts"),
priority=instance_priority,
name_sort_function=instance_sort_key)
algorithms: Final[Styler] = Styler(key_func=get_algorithm,
namer=algorithm_namer,
none_name=Lang.translate("all_algos"),
priority=algorithm_priority,
name_sort_function=algorithm_sort_key)
statistics: Final[Styler] = Styler(key_func=get_statistic,
none_name=Lang.translate("single_run"),
priority=stat_priority,
name_sort_function=stat_sort_key)
x_dim: str | None = None
y_dim: str | None = None
progress_list: list[Progress] = []
statrun_list: list[StatRun] = []
# First pass: find out the statistics, instances, algorithms, and types
for prg in progresses:
instances.add(prg)
algorithms.add(prg)
statistics.add(prg)
if isinstance(prg, Progress):
progress_list.append(prg)
elif isinstance(prg, StatRun):
statrun_list.append(prg)
else:
raise type_error(prg, "progress plot element",
(Progress, StatRun))
# Validate that we have consistent time and objective units.
if x_dim is None:
x_dim = prg.time_unit
elif x_dim != prg.time_unit:
raise ValueError(
f"Time units {x_dim} and {prg.time_unit} do not fit!")
if y_dim is None:
y_dim = prg.f_name
elif y_dim != prg.f_name:
raise ValueError(
f"F-units {y_dim} and {prg.f_name} do not fit!")
del progresses
if (len(progress_list) + len(statrun_list)) <= 0:
raise ValueError("Empty input data?")
if (x_dim is None) or (y_dim is None):
raise ValueError("Illegal state?")
instances.finalize()
algorithms.finalize()
statistics.finalize()
# pick the right sorting order
sf: Callable[[StatRun | Progress], Any] = sort_key
if (instances.count > 1) and (algorithms.count == 1) \
and (statistics.count == 1):
def __x1(r: StatRun | Progress, ssf=instance_sort_key) -> Any:
return ssf(r.instance)
sf = __x1
elif (instances.count == 1) and (algorithms.count > 1) \
and (statistics.count == 1):
def __x2(r: StatRun | Progress, ssf=algorithm_sort_key) -> Any:
return ssf(r.algorithm)
sf = __x2
elif (instances.count == 1) and (algorithms.count == 1) \
and (statistics.count > 1):
def __x3(r: StatRun | Progress, ssf=stat_sort_key) -> Any:
return ssf(r.instance)
sf = __x3
elif (instances.count > 1) and (algorithms.count > 1):
def __x4(r: StatRun | Progress, sas=algorithm_sort_key,
ias=instance_sort_key,
ag=algorithm_priority > instance_priority) \
-> tuple[Any, Any]:
k1 = ias(r.instance)
k2 = sas(r.algorithm)
return (k2, k1) if ag else (k1, k2)
sf = __x4
statrun_list.sort(key=sf)
progress_list.sort()
def __set_importance(st: Styler) -> None:
if st is statistics:
none = -1
not_none = 1
else:
none = 1
not_none = 0
none_lw = importance_to_line_width_func(none)
not_none_lw = importance_to_line_width_func(not_none)
st.set_line_width(lambda x: [none_lw if i <= 0 else not_none_lw
for i in range(x)])
none_a = importance_to_alpha_func(none)
not_none_a = importance_to_alpha_func(not_none)
st.set_line_alpha(lambda x: [none_a if i <= 0 else not_none_a
for i in range(x)])
# determine the style groups
groups: list[Styler] = []
no_importance = True
if instances.count > 1:
groups.append(instances)
if algorithms.count > 1:
groups.append(algorithms)
add_stat_to_groups = False
if statistics.count > 1:
if statistics.has_none and (statistics.count == 2):
__set_importance(statistics)
no_importance = False
add_stat_to_groups = True
else:
groups.append(statistics)
if len(groups) > 0:
groups.sort()
groups[0].set_line_color(distinct_colors_func)
if len(groups) > 1:
groups[1].set_line_dash(distinct_line_dashes_func)
if (len(groups) > 2) and no_importance:
g = groups[2]
if g.count > 2:
raise ValueError(
f"Cannot have {g.count} importance values.")
__set_importance(g)
no_importance = False
elif color_algorithms_as_fallback_group:
algorithms.set_line_color(distinct_colors_func)
groups.append(algorithms)
if add_stat_to_groups:
groups.append(statistics)
# If we only have <= 2 groups, we can mark None and not-None values with
# different importance.
if no_importance and statistics.has_none and (statistics.count > 1):
__set_importance(statistics)
no_importance = False
if no_importance and instances.has_none and (instances.count > 1):
__set_importance(instances)
no_importance = False
if no_importance and algorithms.has_none and (algorithms.count > 1):
__set_importance(algorithms)
# we will collect all lines to plot in plot_list
plot_list: list[dict] = []
# first we collect all progress object
for prgs in progress_list:
style = pd.create_line_style()
for g in groups:
g.add_line_style(prgs, style)
style["x"] = prgs.time
style["y"] = prgs.f
plot_list.append(style)
del progress_list
# now collect the plot data for the statistics
for sn in statistics.keys:
if sn is None:
continue
for sr in statrun_list:
if statistics.key_func(sr) != sn:
continue
style = pd.create_line_style()
for g in groups:
g.add_line_style(sr, style)
style["x"] = sr.stat[:, 0]
style["y"] = sr.stat[:, 1]
plot_list.append(style)
del statrun_list
font_size_0: Final[float] = importance_to_font_size_func(0)
# set up the graphics area
axes: Final[Axes] = pu.get_axes(figure)
axes.tick_params(axis="x", labelsize=font_size_0)
axes.tick_params(axis="y", labelsize=font_size_0)
# draw the grid
if x_grid or y_grid:
grid_lwd = importance_to_line_width_func(-1)
if x_grid:
axes.grid(axis="x", color=pd.GRID_COLOR, linewidth=grid_lwd)
if y_grid:
axes.grid(axis="y", color=pd.GRID_COLOR, linewidth=grid_lwd)
# set up the axis rangers
if callable(x_axis):
x_axis = x_axis(x_dim)
if not isinstance(x_axis, AxisRanger):
raise type_error(x_axis, "x_axis", AxisRanger)
if callable(y_axis):
y_axis = y_axis(y_dim)
if not isinstance(y_axis, AxisRanger):
raise type_error(y_axis, "y_axis", AxisRanger)
# plot the lines
for line in plot_list:
axes.step(where="post", **line)
x_axis.register_array(line["x"])
y_axis.register_array(line["y"])
del plot_list
x_axis.apply(axes, "x")
y_axis.apply(axes, "y")
if legend:
handles: list[Artist] = []
for g in groups:
g.add_to_legend(handles.append)
g.has_style = False
if instances.has_style:
instances.add_to_legend(handles.append)
if algorithms.has_style:
algorithms.add_to_legend(handles.append)
if statistics.has_style:
statistics.add_to_legend(handles.append)
if len(handles) > 0:
axes.legend(loc="upper right",
handles=handles,
labelcolor=[art.color if hasattr(art, "color")
else pd.COLOR_BLACK for art in handles],
fontsize=font_size_0)
pu.label_axes(axes=axes,
x_label=x_label(x_dim) if callable(x_label) else x_label,
x_label_inside=x_label_inside,
x_label_location=x_label_location,
y_label=y_label(y_dim) if callable(y_label) else y_label,
y_label_inside=y_label_inside,
y_label_location=y_label_location,
font_size=font_size_0)
return axes