ml_plot_iris.py

# https://scikit-learn.org/0.18/auto_examples/svm/plot_iris.html
# Copyright 2010-2016, scikit-learn developers
# Copyright 2022 Roland Richter
# used and released under BSD 3-Clause License

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap

from sklearn import datasets
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import Perceptron
from sklearn.tree import DecisionTreeClassifier
from sklearn import svm
from sklearn.neural_network import MLPClassifier

# import iris dataset, but use only two features
features = [0, 2]
iris = datasets.load_iris()
X = iris.data[:, features]
labels = iris.target

# create instances of methods, and fit data
nbrs = KNeighborsClassifier(n_neighbors=5).fit(X, labels)
dtree = DecisionTreeClassifier().fit(X, labels)
ptron = Perceptron(shuffle=True).fit(X, labels)
svm_lin = svm.LinearSVC(C=1.0).fit(X, labels)
svm_rbf = svm.SVC(kernel="rbf", gamma=0.7, C=1.0).fit(X, labels)
nn_mlp = MLPClassifier(
    shuffle=True, hidden_layer_sizes=(20,), activation="tanh", random_state=42
).fit(X, labels)

# create a mesh to plot in
h = 0.02  # step size in the mesh
x_min, x_max = X[:, 0].min() - 0.3, X[:, 0].max() + 0.3
y_min, y_max = X[:, 1].min() - 0.3, X[:, 1].max() + 0.3
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))

# title for the plots
titles = [
    "k nearest neighbours",
    "decision tree",
    "perceptron",
    "SVM with linear kernel",
    "SVM with RBF kernel",
    "multi-layer perceptron",
]

colormap = ListedColormap(["red", "green", "blue"])

for i, clf in enumerate((nbrs, dtree, ptron, svm_lin, svm_rbf, nn_mlp)):
    # Plot the decision boundary. For that, we will assign a color to each
    # point in the mesh [x_min, x_max]x[y_min, y_max].
    plt.subplot(2, 3, i + 1)
    plt.subplots_adjust(wspace=0.3, hspace=0.4)

    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

    # put the result into a color plot
    Z = Z.reshape(xx.shape)
    plt.contourf(xx, yy, Z, cmap=colormap, alpha=0.3)

    # plot the training points
    plt.scatter(X[:, 0], X[:, 1], s=4.0, c=labels, cmap=colormap)
    plt.xlabel(iris.feature_names[features[0]])
    plt.ylabel(iris.feature_names[features[1]])
    plt.xlim(xx.min(), xx.max())
    plt.ylim(yy.min(), yy.max())
    plt.xticks(())
    plt.yticks(())
    plt.title(titles[i])

plt.show()