Show evaluation metrics in decision boundary plot - matplotlib

Question

I am working on imbalanced classification. I wanted to add g-mean, and accuracy in my decision boundary plot. It would be nice to see the differences of these scoring metrics in plot. I don't see any option to compute these scores within this decision boundary plot. Is there way I can add this extra information in my decision boundary plot. I appreciate your time. Thanks!
import numpy as np
import matplotlib.pyplot as plt
from mlxtend.plotting import plot_decision_regions
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_blobs
from sklearn.metrics import make_scorer
from imblearn.metrics import geometric_mean_score
from mlxtend.plotting import plot_decision_regions
import matplotlib.gridspec as gridspec
import itertools
gmean = make_scorer(geometric_mean_score, greater_is_better=True)
scoring = {'G-mean': gmean, 'Accuracy':'accuracy'}
X, y = make_blobs(n_samples=[1000, 10],centers=[[0.0, 0.0], [2.0, 2.0]],cluster_std= [1.5, 0.5],random_state=0, shuffle=False)
clf1 = LogisticRegression(max_iter=100000)
clf2 = LogisticRegression(class_weight="balanced",max_iter=100000)
gs = gridspec.GridSpec(2, 2)
fig = plt.figure(figsize=(10,8))
labels = ['Logistic Regression', 'Weighted Logistic Regression']
for clf, lab, grd in zip([clf1, clf2],
labels,
itertools.product([0, 1], repeat=2)):
clf.fit(X, y)
ax = plt.subplot(gs[grd[0], grd[1]])
fig = plot_decision_regions(X=X, y=y, clf=clf, legend=2)
plt.title(lab)
plt.show()

Answer 1

You can use plt.text() to add g-mean, and accuracy in your decision boundary plot.
For example:
gs = gridspec.GridSpec(2, 2)
fig = plt.figure(figsize=(15, 8))
labels = ['Logistic Regression', 'Weighted Logistic Regression']
for clf, lab, grd in zip([clf1, clf2],
labels,
itertools.product([0, 1], repeat=2)):
clf.fit(X, y)
ax = plt.subplot(gs[grd[0], grd[1]])
ax.text(6, 4, "gmean : ", fontsize=10)
ax.text(6, 2, "accuracy : ", fontsize=10)
fig = plot_decision_regions(X=X, y=y, clf=clf, legend=2)
plt.title(lab)
plt.show()