niklas_temp.py

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.colors import ListedColormap
from sklearn import neighbors, datasets
from sklearn.metrics import classification_report

import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC

n_neighbors = 15

# import some data to play with
iris = datasets.load_iris()

# we only take the first two features. We could avoid this ugly
# slicing by using a two-dim dataset
X = iris.data[:, [0,3]]
y = iris.target

h = 0.02  # step size in the mesh

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

# Create color maps
cmap_light = ListedColormap(["orange", "cyan", "cornflowerblue"])
cmap_bold = ["darkorange", "c", "darkblue"]

for weights in ["uniform", "distance"]:
    # we create an instance of Neighbours Classifier and fit the data.
    clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
    clf.fit(X, y)

    # 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].
    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

    cm = confusion_matrix(y_train, Z, labels=clf.classes_)
    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=clf.classes_)

    # Put the result into a color plot
    Z = Z.reshape(xx.shape)
    plt.figure(figsize=(8, 6))
    plt.contourf(xx, yy, Z, cmap=cmap_light)

    # Plot also the training points
    sns.scatterplot(
        x=X[:, 0],
        y=X[:, 1],
        hue=iris.target_names[y],
        palette=cmap_bold,
        alpha=1.0,
        edgecolor="black",
    )
    plt.xlim(xx.min(), xx.max())
    plt.ylim(yy.min(), yy.max())
    plt.title(
        "3-Class classification (k = %i, weights = '%s')" % (n_neighbors, weights)
    )
    plt.xlabel(iris.feature_names[0])
    plt.ylabel(iris.feature_names[1])

print(classification_report(y, clf.predict(X)))
disp.plot()
plt.show()