import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score, davies_bouldin_score
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.preprocessing import LabelEncoder

# Create a synthetic dataset
X, y_true = datasets.make_blobs(n_samples=300, centers=3, cluster_std=0.60, random_state=0)

# Fit the K-Means algorithm
kmeans = KMeans(n_clusters=3, random_state=0)
y_kmeans = kmeans.fit_predict(X)

# Performance Metrics
silhouette_avg = silhouette_score(X, y_kmeans)
davies_bouldin = davies_bouldin_score(X, y_kmeans)
print("Silhouette Score: ", silhouette_avg)
print("Davies-Bouldin Score: ", davies_bouldin)

# Confusion Matrix and Classification Report (if true labels are available)
# Relabel the clusters to match the true labels
def relabel_clusters(y_true, y_pred):
    label_encoder = LabelEncoder()
    y_true_encoded = label_encoder.fit_transform(y_true)
    cm = confusion_matrix(y_true_encoded, y_pred)
    return np.argmax(cm, axis=1)

relabel_map = relabel_clusters(y_true, y_kmeans)
y_kmeans_mapped = np.array([relabel_map[label] for label in y_kmeans])

# Display confusion matrix and classification report
print("Confusion Matrix:\n", confusion_matrix(y_true, y_kmeans_mapped))
print("Classification Report:\n", classification_report(y_true, y_kmeans_mapped))

# Visualization of the clusters
plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=50, cmap='viridis')
centers = kmeans.cluster_centers_
plt.scatter(centers[:, 0], centers[:, 1], c='red', s=200, alpha=0.75, marker='X')
plt.title('K-Means Clustering')
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.show()