from numbers import Real
from typing import Dict, Iterable, List, Optional, Sequence, Tuple, Union
import matplotlib.axes
import matplotlib.pyplot as plt
import numpy as np
from ..result import Result
from .clust_color import assign_colors, assign_colors_for_list, delete_nan_inf
from .misc import process_result_list, process_start_indices
[docs]def optimization_run_properties_one_plot(
results: Result,
properties_to_plot: Optional[List[str]] = None,
size: Tuple[float, float] = (18.5, 10.5),
start_indices: Optional[Union[int, Iterable[int]]] = None,
colors: Optional[Union[List[float], List[List[float]]]] = None,
legends: Optional[Union[str, List[str]]] = None,
plot_type: str = 'line'
) -> matplotlib.axes.Axes:
"""
Plot stats for allproperties specified in properties_to_plot on one plot.
Parameters
----------
results:
Optimization result obtained by 'optimize.py' or list of those
properties_to_plot:
Optimization run properties that should be plotted
size:
Figure size (width, height) in inches. Is only applied when no ax
object is specified
start_indices:
List of integers specifying the multistarts to be plotted or
int specifying up to which start index should be plotted
colors:
List of RGBA colors (one color per property in properties_to_plot),
or single RGBA color. If not set and one result, clustering is done
and colors are assigned automatically
legends:
Labels, one label per optimization property
plot_type:
Specifies plot type. Possible values: 'line' and 'hist'
Returns
-------
ax:
The plot axes.
Examples
--------
optimization_properties_per_multistart(
result1,
properties_to_plot=['time'],
colors=[.5, .9, .9, .3])
optimization_properties_per_multistart(
result1,
properties_to_plot=['time', 'n_grad'],
colors=[[.5, .9, .9, .3], [.2, .1, .9, .5]])
"""
if properties_to_plot is None:
properties_to_plot = ['time', 'n_fval', 'n_grad', 'n_hess', 'n_res',
'n_sres']
if colors is None:
colors = assign_colors_for_list(len(properties_to_plot))
elif len(colors) == 4 and isinstance(colors[0], Real):
colors = [colors]
if len(colors) != len(properties_to_plot):
raise ValueError('Number of RGBA colors should be the same as number '
'of optimization properties to plot')
if legends is None:
legends = properties_to_plot
elif not isinstance(legends, list):
legends = [legends]
if len(legends) != len(properties_to_plot):
raise ValueError('Number of legends should be the same as number of '
'optimization properties to plot')
ax = plt.subplots()[1]
fig = plt.gcf()
fig.set_size_inches(*size)
for idx, prop_name in enumerate(properties_to_plot):
optimization_run_property_per_multistart(
results, prop_name, ax, size, start_indices, colors[idx],
legends[idx], plot_type)
ax.set_ylabel("property value")
ax.set_title("Optimization properties per optimization run")
return ax
[docs]def optimization_run_properties_per_multistart(
results: Union[Result, Sequence[Result]],
properties_to_plot: Optional[List[str]] = None,
size: Tuple[float, float] = (18.5, 10.5),
start_indices: Optional[Union[int, Iterable[int]]] = None,
colors: Optional[Union[List[float], List[List[float]]]] = None,
legends: Optional[Union[str, List[str]]] = None,
plot_type: str = 'line'
) -> Dict[str, plt.Subplot]:
"""
One plot per optimization property in properties_to_plot.
Parameters
----------
results:
Optimization result obtained by 'optimize.py' or list of those
properties_to_plot:
Optimization run properties that should be plotted
size:
Figure size (width, height) in inches. Is only applied when no ax
object is specified
start_indices:
List of integers specifying the multistarts to be plotted or
int specifying up to which start index should be plotted
colors:
List of RGBA colors (one color per result in results),
or single RGBA color. If not set and one result, clustering is done
and colors are assigned automatically
legends:
Labels for line plots, one label per result object
plot_type:
Specifies plot type. Possible values: 'line' and 'hist'
Returns
-------
ax:
The plot axes.
Examples
--------
optimization_properties_per_multistart(
result1,
properties_to_plot=['time'],
colors=[.5, .9, .9, .3])
optimization_properties_per_multistart(
[result1, result2],
properties_to_plot=['time'],
colors=[[.5, .9, .9, .3], [.2, .1, .9, .5]])
optimization_properties_per_multistart(
result1,
properties_to_plot=['time', 'n_grad'],
colors=[.5, .9, .9, .3])
optimization_properties_per_multistart(
[result1, result2], properties_to_plot=['time', 'n_fval'],
colors=[[.5, .9, .9, .3], [.2, .1, .9, .5]])
"""
if properties_to_plot is None:
properties_to_plot = ['time', 'n_fval', 'n_grad', 'n_hess', 'n_res',
'n_sres']
num_subplot = len(properties_to_plot)
# compute, how many rows and columns we need for the subplots
num_row = int(np.round(np.sqrt(num_subplot)))
num_col = int(np.ceil(num_subplot / num_row))
fig, axes = plt.subplots(num_row, num_col, squeeze=False)
fig.set_size_inches(*size)
for ax in axes.flat[num_subplot:]:
ax.remove()
axes = dict(zip(range(num_subplot), axes.flat))
for idx, prop_name in enumerate(properties_to_plot):
ax = axes[idx]
optimization_run_property_per_multistart(
results, prop_name, ax, size, start_indices, colors, legends,
plot_type)
return axes
[docs]def optimization_run_property_per_multistart(
results: Union[Result, Sequence[Result]],
opt_run_property: str,
axes: Optional[matplotlib.axes.Axes] = None,
size: Tuple[float, float] = (18.5, 10.5),
start_indices: Optional[Union[int, Iterable[int]]] = None,
colors: Optional[Union[List[float], List[List[float]]]] = None,
legends: Optional[Union[str, List[str]]] = None,
plot_type: str = 'line') -> matplotlib.axes.Axes:
"""
Plot stats for an optimization run property specified by opt_run_property.
It is possible to plot a histogram or a line plot. In a line plot,
on the x axis are the numbers of the multistarts, where the multistarts are
ordered with respect to a function value. On the y axis of the line plot
the value of the corresponding parameter for each multistart is displayed.
Parameters
----------
opt_run_property:
optimization run property to plot.
One of the 'time', 'n_fval', 'n_grad', 'n_hess', 'n_res', 'n_sres'
results:
Optimization result obtained by 'optimize.py' or list of those
axes:
Axes object to use
size:
Figure size (width, height) in inches. Is only applied when no ax
object is specified
start_indices:
List of integers specifying the multistarts to be plotted or
int specifying up to which start index should be plotted
colors:
List of RGBA colors (one color per result in results),
or single RGBA color. If not set and one result, clustering is done
and colors are assigned automatically
legends:
Labels for line plots, one label per result object
plot_type:
Specifies plot type. Possible values: 'line', 'hist', 'both'
Returns
-------
ax:
The plot axes.
"""
supported_properties = {
'time': 'Wall-clock time (seconds)',
'n_fval': 'Number of function evaluations',
'n_grad': 'Number of gradient evaluations',
'n_hess': 'Number of Hessian evaluations',
'n_res': 'Number of residuals evaluations',
'n_sres': 'Number of residual sensitivity evaluations'
}
if opt_run_property not in supported_properties:
raise ValueError("Wrong value of opt_run_property. Only the following "
"values are allowed: 'time', 'n_fval', 'n_grad', "
"'n_hess', 'n_res', 'n_sres'")
# parse input
(results, colors, legends) = process_result_list(results, colors, legends)
# axes
if axes is None:
ncols = 2 if plot_type == 'both' else 1
fig, axes = plt.subplots(1, ncols)
fig.set_size_inches(*size)
fig.suptitle(
f'{supported_properties[opt_run_property]} per optimizer run')
else:
axes.set_title(
f'{supported_properties[opt_run_property]} per optimizer run')
# loop over results
for j, result in enumerate(results):
if plot_type == 'both':
axes[0] = stats_lowlevel(result, opt_run_property,
supported_properties[opt_run_property],
axes[0], start_indices, colors[j],
legends[j])
axes[1] = stats_lowlevel(result, opt_run_property,
supported_properties[opt_run_property],
axes[1], start_indices, colors[j],
legends[j], plot_type='hist')
else:
axes = stats_lowlevel(result, opt_run_property,
supported_properties[opt_run_property], axes,
start_indices, colors[j], legends[j],
plot_type)
if sum((legend is not None for legend in legends)) > 0:
if plot_type == 'both':
for ax in axes:
ax.legend()
else:
axes.legend()
return axes
def stats_lowlevel(result: Result,
property_name: str,
axis_label: str,
ax: matplotlib.axes.Axes,
start_indices: Optional[Union[int, Iterable[int]]] = None,
color: Union[str, List[float], List[List[float]]] = 'C0',
legend: Optional[str] = None,
plot_type: str = 'line'):
"""
Plot values of the optimization run property across different multistarts.
Parameters
----------
result:
Optimization result obtained by 'optimize.py'
property_name:
name of the optimization result property which value should be plotted
axis_label:
Label for the y axis of the line plot or x axis of the histogram
ax:
Axes object to use
start_indices:
List of integers specifying the multistarts to be plotted or
int specifying up to which start index should be plotted
color:
List of RGBA colors (length equal to the number of multistarts),
or single color, defined by a string or RGBA list
If not set, then for the line plot clustering is done and
colors are assigned automatically
legend:
Label describing the result
plot_type:
Specifies plot type. Possible values: 'line' and 'hist'
Returns
-------
ax:
The plot axes.
"""
fvals = result.optimize_result.get_for_key('fval')
values = result.optimize_result.get_for_key(property_name)
values, fvals = delete_nan_inf(fvals, values)
if start_indices is not None:
start_indices = process_start_indices(start_indices, len(values))
values = values[start_indices]
fvals = fvals[start_indices]
n_starts = len(values)
# assign colors
colors = assign_colors(vals=fvals, colors=color,
balance_alpha=False)
# sort TODO: issue # 378
sorted_indices = sorted(range(n_starts), key=lambda j: fvals[j])
values = values[sorted_indices]
if plot_type == 'line':
# plot line
ax.plot(range(n_starts), values, color=[0.7, 0.7, 0.7, 0.6])
# plot points
for i, v in enumerate(values):
if i == 0:
tmp_legend = legend
else:
tmp_legend = None
ax.scatter(i, v, color=colors[i], marker='o', label=tmp_legend)
ax.set_xlabel('Ordered optimizer run')
ax.set_ylabel(axis_label)
else:
ax.hist(values, color=color, bins='auto', label=legend)
ax.set_xlabel(axis_label)
ax.set_ylabel('Number of multistarts')
return ax