from typing import Optional, Tuple
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from ..result import Result
[docs]def optimizer_convergence(result: Result,
ax: Optional[plt.Axes] = None,
xscale: str = 'symlog',
yscale: str = 'log',
size: Tuple[float] = (18.5, 10.5)) -> plt.Axes:
"""
Visualize to help spotting convergence issues.
Scatter plot of function values and gradient values at the end of
optimization. Optimizer exit-message is encoded by color. Can help
identifying convergence issues in optimization and guide tolerance
refinement etc.
Parameters
----------
result:
Optimization result obtained by 'optimize.py'
ax:
Axes object to use.
size:
Figure size (width, height) in inches. Is only applied when no ax
object is specified
xscale:
Scale for x-axis
yscale:
Scale for y-axis
Returns
-------
ax: matplotlib.Axes
The plot axes.
"""
if ax is None:
ax = plt.subplots(figsize=size)[1]
fvals = result.optimize_result.get_for_key('fval')
grad_norms = [
np.linalg.norm(
result.problem.get_reduced_vector(grad,
result.problem.x_free_indices),
2
)
if grad is not None else np.NaN
for grad in result.optimize_result.get_for_key('grad')
]
msgs = result.optimize_result.get_for_key('message')
conv_data = pd.DataFrame({
'fval': fvals,
'gradient norm': grad_norms,
'exit message': msgs
})
sns.scatterplot(x='fval', y='gradient norm', hue='exit message',
data=conv_data, ax=ax)
ax.set_yscale(yscale)
ax.set_xscale(xscale)
return ax