03 聚類算法 - K-means聚類
04 聚類算法 - 代碼案例一 - K-means聚類
05 聚類算法 - 二分K-Means秦忿、K-Means++麦射、K-Means||灯谣、Canopy、Mini Batch K-Means算法
06 聚類算法 - 代碼案例二 - K-Means算法和Mini Batch K-Means算法比較
需求： 基于scikit包中的創(chuàng)建模擬數(shù)據(jù)的API創(chuàng)建聚類數(shù)據(jù)半等，對K-Means算法和Mini Batch K-Means算法構(gòu)建的模型進行評估揍愁。
相關(guān)API
常規(guī)操作：
import time
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from sklearn.cluster import MiniBatchKMeans, KMeans
from sklearn import metrics
from sklearn.metrics.pairwise import pairwise_distances_argmin
from sklearn.datasets.samples_generator import make_blobs
## 設(shè)置屬性防止中文亂碼
mpl.rcParams['font.sans-serif'] = [u'SimHei']
mpl.rcParams['axes.unicode_minus'] = False
一莽囤、構(gòu)建數(shù)據(jù)
centers = [[1, 1], [-1, -1], [1, -1]]
clusters = len(centers)
X, Y = make_blobs(n_samples=3000, centers=centers, cluster_std=0.7, random_state=28)
Y # 在實際工作中是人工給定的切距，專門用于判斷聚類的效果的一個值
array([2, 0, 0, ..., 2, 2, 1])
二、構(gòu)建k-means++模型
k_means = KMeans(init='k-means++', n_clusters=clusters, random_state=28)
t0 = time.time()
k_means.fit(X)
km_batch = time.time() - t0
print ("K-Means算法模型訓練消耗時間:%.4fs" % km_batch)
K-Means算法模型訓練消耗時間:0.1211s
三谜悟、構(gòu)建Mini Batch K-Means模型
batch_size = 100
mbk = MiniBatchKMeans(init='k-means++', n_clusters=clusters,
batch_size=batch_size, random_state=28)
t0 = time.time()
mbk.fit(X)
mbk_batch = time.time() - t0
print ("Mini Batch K-Means算法模型訓練消耗時間:%.4fs" % mbk_batch)
Mini Batch K-Means算法模型訓練消耗時間:0.0991s
km_y_hat = k_means.labels_
mbkm_y_hat = mbk.labels_
print(km_y_hat) # 樣本所屬的類別
[0 2 2 ... 1 1 0]
k_means_cluster_centers = k_means.cluster_centers_
mbk_means_cluster_centers = mbk.cluster_centers_
print ("K-Means算法聚類中心點:\ncenter=", k_means_cluster_centers)
print ("Mini Batch K-Means算法聚類中心點:\ncenter=", mbk_means_cluster_centers)
order = pairwise_distances_argmin(k_means_cluster_centers,
mbk_means_cluster_centers)
order
K-Means算法聚類中心點:
center= [[-1.0600799 -1.05662982]
[ 1.02975208 -1.07435837]
[ 1.01491055 1.02216649]]
Mini Batch K-Means算法聚類中心點:
center= [[ 0.99602094 1.10688195]
[-1.00828286 -1.05983915]
[ 1.07892315 -0.94286826]]
array([1, 2, 0], dtype=int64)
效果評估:
score_funcs = [
metrics.adjusted_rand_score,#ARI
metrics.v_measure_score,#均一性和完整性的加權(quán)平均
metrics.adjusted_mutual_info_score,#AMI
metrics.mutual_info_score,#互信息
]
迭代對每個評估函數(shù)進行評估操作
for score_func in score_funcs:
t0 = time.time()
km_scores = score_func(Y,km_y_hat)
print("K-Means算法:%s評估函數(shù)計算結(jié)果值:%.5f葡幸；計算消耗時間:%0.3fs" %
(score_func.__name__,km_scores, time.time() - t0))
t0 = time.time()
mbkm_scores = score_func(Y,mbkm_y_hat)
print("Mini Batch K-Means算法:%s評估函數(shù)計算結(jié)果值:%.5f；計算消耗時間:%0.3fs\n" %
(score_func.__name__,mbkm_scores, time.time() - t0))
