svm_visualization.py

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler

# Load dataset
iris = datasets.load_iris()
X = iris.data[:, :2]  # We only use the first two features for visualization
y = iris.target

# Binary classification for visualization simplicity
X = X[y != 2]
y = y[y != 2]

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Standardize the features
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Train an SVM model
model = SVC(kernel='linear', C=1)
model.fit(X_train, y_train)

# Visualization function
def plot_svm_decision_boundary(model, X, y):
    plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)
    
    # Plot the decision boundary
    ax = plt.gca()
    xlim = ax.get_xlim()
    ylim = ax.get_ylim()
    
    # Create grid to evaluate model
    xx = np.linspace(xlim[0], xlim[1], 50)
    yy = np.linspace(ylim[0], ylim[1], 50)
    YY, XX = np.meshgrid(yy, xx)
    xy = np.vstack([XX.ravel(), YY.ravel()]).T
    Z = model.decision_function(xy).reshape(XX.shape)
    
    # Plot decision boundary and margins
    ax.contour(XX, YY, Z, colors='k', levels=[-1, 0, 1], alpha=0.5,
               linestyles=['--', '-', '--'])
    
    # Plot support vectors
    ax.scatter(model.support_vectors_[:, 0], model.support_vectors_[:, 1], s=100,
               linewidth=1, facecolors='none', edgecolors='k')

# Plot decision boundary with training data
plt.figure(figsize=(8, 6))
plot_svm_decision_boundary(model, X_train, y_train)
plt.title('SVM Decision Boundary with Support Vectors')
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.show()