How do I plot boundary decisions of multiple classifiers in one figure?

Question

I want to plot the decision boundary conditions for multiple decision grain boundary in the same figure

The code is as follows:

import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.inspection import DecisionBoundaryDisplay
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis,QuadraticDiscriminantAnalysis
from sklearn.ensemble import AdaBoostClassifier,BaggingClassifier,RandomForestClassifier,HistGradientBoostingClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import VotingClassifier,ExtraTreesClassifier

iris = load_iris()
X = iris.data[:, :2]
classifiers = [LogisticRegression(solver='sag',penalty='l2',multi_class='ovr',
            max_iter=25000,random_state=None,fit_intercept=True),
            LinearDiscriminantAnalysis(),
            QuadraticDiscriminantAnalysis(),
            DecisionTreeClassifier(min_samples_leaf=1),
            BaggingClassifier(),
            RandomForestClassifier(),
            AdaBoostClassifier(),
            HistGradientBoostingClassifier(),
            VotingClassifier(estimators=[('rfc',RandomForestClassifier()),
                                         ('dtc',DecisionTreeClassifier())],voting ='soft'),
            ExtraTreesClassifier()]

for classifier in classifiers:
    classifier.fit(X,iris.target)
    disp = DecisionBoundaryDisplay.from_estimator(classifier, X, response_method="predict", xlabel=iris.feature_names[0], ylabel=iris.feature_names[1], alpha=0.5)
    disp.ax_.scatter(X[:, 0], X[:, 1], c=iris.target, edgecolor="k")

plt.show()

I did not get the result that I want, I need those plots in the same figure.

Can someone help me in this case?

@medium-dimensional can you also tell me how would someone add a legend with y categories, on my data set it is either 0 or 1 — ala mazahreh, Nov 01 '22 at 18:09
Could you please make sure the code reproduces the image that you are getting? It's throwing an error *NameError: name 'DecisionTreeClassifier' is not defined*. — medium-dimensional, Nov 01 '22 at 18:18
@medium-dimensional in this case, if it is possible to add legends based on the y categroies, this would be fine, i guess i forgot to import other classifiers — ala mazahreh, Nov 01 '22 at 18:21
@medium-dimensional i have edited the code with the imported classifiers — ala mazahreh, Nov 01 '22 at 18:23

medium-dimensional · Accepted Answer · 2022-11-01T19:43:01.917

0

To get the decision boundaries of different classifiers in one figure, make sure to pass the argument ax in DecisionBoundaryDisplay.from_estimator:

# Assuming there are 10 classifiers
fig, ax = plt.subplots(nrows=5, ncols=2)
ax = ax.T.flatten()
i = 0

for classifier in classifiers:

    classifier.fit(X,iris.target)
    disp = DecisionBoundaryDisplay.from_estimator(classifier, 
    X, response_method="predict", 
    xlabel=iris.feature_names[0], ylabel=iris.feature_names[1],
    alpha=0.5, ax=ax[i])

    disp.ax_.scatter(X[:, 0], X[:, 1], c=iris.target, edgecolor="k")
    
    # Refer to next Axes in a 5 * 2 grid created by plt.subplots
    i += 1
    
    # To get labels for contour plots
    labels = np.unique(iris.target)
    proxy = [plt.Rectangle((0,0),1,1,fc = pc.get_facecolor()[0]) for pc in disp.surface_.collections]
    disp.ax_.legend(proxy, labels)

plt.show()

This gives:

This answer was motivated by this answer and this answer.

edited Nov 01 '22 at 19:43

answered Nov 01 '22 at 18:49

medium-dimensional

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could you add a code to show the legend of the categories? – ala mazahreh Nov 01 '22 at 19:04
1

yes, the labels of the categories – ala mazahreh Nov 01 '22 at 19:09
@alamazahreh You can see that labels doesn't have exact colors as of contours, so that needs to be fixed. Feel free to unaccept this answer. ;) – medium-dimensional Nov 01 '22 at 20:13
this is exactly what i want! – ala mazahreh Nov 01 '22 at 20:17
1

yes, you are right – ala mazahreh Nov 02 '22 at 10:03

How do I plot boundary decisions of multiple classifiers in one figure?

1 Answers1