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import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from collections.abc import Sequence
from matplotlib.figure import Figure
def plot_cluster_counts(labels: Sequence[int]) -> Figure:
"""
Generate a bar chart showing the number of samples in each cluster.
Args:
labels: Sequence of integer cluster labels.
Returns:
Matplotlib Figure with cluster size distribution.
"""
# Count and sort cluster sizes
counts = pd.Series(labels).value_counts().sort_index()
# Create bar chart
fig, ax = plt.subplots(figsize=(8, 5))
ax.bar(counts.index.astype(str), counts.values, edgecolor="black")
ax.set_title("Cluster Size Distribution", fontsize=14, fontweight="bold")
ax.set_xlabel("Cluster Label", fontsize=12)
ax.set_ylabel("Number of Samples", fontsize=12)
ax.grid(axis="y", linestyle="--", alpha=0.6)
plt.tight_layout()
return fig
def visualize_clusters(
X: np.ndarray,
labels: Sequence[int],
centers: np.ndarray
) -> Figure:
"""
Scatter plot of clustered data with centroids.
Args:
X: 2D array of shape (n_samples, 2).
labels: Cluster labels for each sample.
centers: 2D array of cluster centroids.
Returns:
Matplotlib Figure with clusters and centroids plotted.
"""
unique_labels = np.unique(labels)
n_clusters = unique_labels.size
# Choose a colormap
cmap = plt.get_cmap('tab10')
fig, ax = plt.subplots(figsize=(8, 6))
for idx, cluster in enumerate(unique_labels):
mask = labels == cluster
ax.scatter(
X[mask, 0], X[mask, 1],
s=50,
label=f"Cluster {cluster}",
color=cmap(idx),
edgecolor='k',
alpha=0.7
)
# Plot centroids
ax.scatter(
centers[:, 0], centers[:, 1],
s=200,
marker='X',
c='black',
label='Centroids',
linewidths=2
)
ax.set_title("Cluster Visualization", fontsize=14, fontweight="bold")
ax.set_xlabel('Annual Income ($K)', fontsize=14)
ax.set_xlabel('Spending Score', fontsize=14)
ax.legend(title="Clusters", fontsize=10, title_fontsize=12)
ax.grid(True, linestyle="--", alpha=0.6)
plt.tight_layout()
return fig |