用惯性替换轮廓答案

【问题标题】：replace the silhouette with the Inertia用惯性替换轮廓
【发布时间】：2022-06-11 16:51:05
【问题描述】：

我有问题。我正在使用 k-means 并希望找到最佳集群。不幸的是，我的数据集太大而无法应用silhouette 。是否可以调整此代码并将silhouette 替换为Inertia？

MVC

from sklearn.cluster import KMeans
import numpy as np
from sklearn.metrics import silhouette_score
import matplotlib as mpl
import matplotlib.pyplot as plt

X = np.array([[1, 2], [1, 4], [1, 0],
              [10, 2], [10, 4], [10, 0],
              [10, 2], [10, 4], [10, 0],
              [1, 2], [1, 4], [1, 0],
              [10, 2], [10, 4], [10, 0],
              [10, 2], [10, 4], [10, 0],
              [1, 2], [1, 4], [1, 0],
              [10, 2], [10, 4], [10, 0],
              [10, 2], [10, 4], [10, 0],
              [1, 2], [1, 4], [1, 0],])

kmeans_per_k = [KMeans(n_clusters=k, random_state=42).fit(X)
                for k in range(1, 10)]
inertias = [model.inertia_ for model in kmeans_per_k]

silhouette_scores = [silhouette_score(X, model.labels_)
                     for model in kmeans_per_k[1:]]


from sklearn.metrics import silhouette_samples
from matplotlib.ticker import FixedLocator, FixedFormatter

plt.figure(figsize=(11, 9))

for k in (3, 4, 5, 6):
    plt.subplot(2, 2, k - 2)
    
    y_pred = kmeans_per_k[k - 1].labels_
    silhouette_coefficients = silhouette_samples(X, y_pred)

    padding = len(X) // 30
    pos = padding
    ticks = []
    for i in range(k):
        coeffs = silhouette_coefficients[y_pred == i]
        coeffs.sort()

        color = mpl.cm.Spectral(i / k)
        plt.fill_betweenx(np.arange(pos, pos + len(coeffs)), 0, coeffs,
                          facecolor=color, edgecolor=color, alpha=0.7)
        ticks.append(pos + len(coeffs) // 2)
        pos += len(coeffs) + padding

    plt.gca().yaxis.set_major_locator(FixedLocator(ticks))
    plt.gca().yaxis.set_major_formatter(FixedFormatter(range(k)))
    if k in (3, 5):
        plt.ylabel("Cluster")
    
    if k in (5, 6):
        plt.gca().set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])
        plt.xlabel("Silhouette Coefficient")
    else:
        plt.tick_params(labelbottom=False)

    plt.axvline(x=silhouette_scores[k - 2], color="red", linestyle="--")
    plt.title("$k={}$".format(k), fontsize=16)

#save_fig("silhouette_analysis_plot")
plt.show()

Inertia 我想要什么

【问题讨论】：

您是否尝试过使用小批量来帮助处理大型数据集scikit-learn.org/stable/modules/generated/…
@MuhammadPathan 问题不在于kMeans 通常需要很长时间，而是度量silhouette 需要很长时间来计算。

标签： python machine-learning cluster-analysis k-means unsupervised-learning