Source code for sklearn_genetic.plots

import logging

logger = logging.getLogger(__name__)  # noqa

# Check if seaborn is installed as an extra requirement
try:
    import seaborn as sns
except ModuleNotFoundError:  # noqa
    logger.error("seaborn not found, pip install seaborn to use plots functions")  # noqa

import numpy as np

from .utils import logbook_to_pandas
from .parameters import Metrics

from .genetic_search import GAFeatureSelectionCV

"""
This module contains some useful function to explore the results of the optimization routines
"""


[docs]def plot_fitness_evolution(estimator, metric="fitness"): """ Parameters ---------- estimator: estimator object A fitted estimator from :class:`~sklearn_genetic.GASearchCV` metric: {"fitness", "fitness_std", "fitness_max", "fitness_min"}, default="fitness" Logged metric into the estimator history to plot Returns ------- Lines plot with the fitness value in each generation """ if metric not in Metrics.list(): raise ValueError(f"metric must be one of {Metrics.list()}, but got {metric} instead") sns.set_style("white") fitness_history = estimator.history[metric] 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(f"{metric.capitalize()} average evolution over generations") ax.set(xlabel="generations", ylabel=f"fitness ({estimator.refit_metric})") 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 """ if isinstance(estimator, GAFeatureSelectionCV): raise TypeError( "Estimator must be a GASearchCV instance, not a GAFeatureSelectionCV instance" ) sns.set_style("white") df = logbook_to_pandas(estimator.logbook) if features: stats = df[features].astype(np.float64) else: variables = [*estimator.space.parameters, estimator.refit_metric] stats = df[variables].select_dtypes(["number", "bool"]).astype(np.float64) 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