import seaborn as sns
from .utils import logbook_to_pandas
"""
This module contains some useful function to explore the results of the optimization routines
"""
[docs]def plot_fitness_evolution(estimator):
"""
Parameters
----------
estimator: estimator object
A fitted estimator from :class:`~sklearn_genetic.GASearchCV`
Returns
-------
Lines plot with the fitness value in each generation
"""
sns.set_style("white")
fitness_history = estimator.history["fitness"]
palette = sns.color_palette("rocket")
sns.set(rc={"figure.figsize": (10, 10)})
ax = sns.lineplot(
x=range(len(estimator)), y=fitness_history, markers=True, palette=palette
)
ax.set_title("Fitness average evolution over generations")
ax.set(xlabel="generations", ylabel=f"fitness ({estimator.scoring})")
return ax
[docs]def plot_search_space(estimator, height=2, s=25, features: list = None):
"""
Parameters
----------
estimator: estimator object
A fitted estimator from :class:`~sklearn_genetic.GASearchCV`
height: float, default=2
Height of each facet
s: float, default=5
Size of the markers in scatter plot
features: list, default=None
Subset of features to plot, if ``None`` it plots all the features by default
Returns
-------
Pair plot of the used hyperparameters during the search
"""
sns.set_style("white")
df = logbook_to_pandas(estimator.logbook)
if features:
stats = df[features]
else:
variables = [*estimator.space.parameters, "score"]
stats = df[variables]
g = sns.PairGrid(stats, diag_sharey=False, height=height)
g = g.map_upper(sns.scatterplot, s=s, color="r", alpha=0.2)
g = g.map_lower(
sns.kdeplot,
shade=True,
cmap=sns.color_palette("ch:s=.25,rot=-.25", as_cmap=True),
)
g = g.map_diag(sns.kdeplot, shade=True, palette="crest", alpha=0.2, color="red")
return g