MeanShift聚类-02python案例

MeanShift聚类-02python案例

Intro

Meanshift的使用案例~

数据引入

from sklearn.cluster import MeanShift, estimate_bandwidthimport matplotlib.pyplot as pltfrom itertools import cycleimport numpy as npimport warningswarnings.filterwarnings("ignore")%matplotlib inline

from sklearn.datasets import load_irisimport pandas as pdpd.set_option('display.max_rows', 500) # 打印最大行数pd.set_option('display.max_columns', 500) # 打印最大列数

# 检查是否是array格式，如果不是，转换成arrayfrom sklearn.utils import check_arrayfrom sklearn.utils import check_random_statefrom sklearn.neighbors import NearestNeighborsfrom sklearn.utils import

iris_df = pd.DataFrame( load_iris()["data"], columns=["sepal_length", "sepal_width", "petal_length", "petal_width"])iris_df["target"] = load_iris()["target"]iris_df.head()

iris_df.groupby(by="target").describe()

从数据上看，三个种类之间，petal_length和petal_width的差异比较大，用它来画图。

# colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')colors =["red","yellow","blue"]marker = ["o","*","+"]for k, col,mark in zip(range(3), colors,marker): sub_data = iris_df.query("target==%s"%k) plt.plot(sub_data.petal_length, sub_data.petal_width,"o", markerfacecolor=col, markeredgecolor='k', markersize=5)plt.show()

可以看到红色点和其余点相差很多，蓝色和黄色有部分点交错在一起

默认参数进行聚类

# ms = MeanShift( bin_seeding=True,cluster_all=False)bandwidth = 0.726ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)ms.fit(iris_df[["petal_length", "petal_width"]])labels = ms.labels_cluster_centers = ms.cluster_centers_labels_unique = np.unique(labels)n_clusters_ = len(labels_unique)print("number of estimated clusters : %d" % n_clusters_)# ############################################################################## Plot resultimport matplotlib.pyplot as pltfrom itertools import cycleplt.figure(1)plt.clf()# colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')colors = ["yellow", "red", "blue"]marker = ["o", "*", "+"]for k, col, mark in zip(range(n_clusters_), colors, marker): my_members = labels == k cluster_center = cluster_centers[k] plt.plot(iris_df[my_members].petal_length, iris_df[my_members].petal_width, ".", markerfacecolor=col, markeredgecolor='k', markersize=6) plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=14) circle = plt.Circle((cluster_center[0], cluster_center[1]), bandwidth, color='black', fill=False) plt.gcf().gca().add_artist(circle)plt.title('Estimated number of clusters: %d' % n_clusters_)plt.show()

number of estimated clusters : 3

从图上看，红色部分自成一派，聚类效果就好，蓝黄两类互有交叉，以最靠近的类别中心来打label.

estimate_bandwidth方法

根据聚类的原始数据，生成建议的bandwidth，基础逻辑：

先抽样，获取部分样本计算这样样本和所有点的最大距离对距离求平均

从逻辑上看，更像是找一个较大的距离，使得能涵盖更多的点

estimate_bandwidth(iris_df[["petal_length", "petal_width"]])

0.7266371274126329

计算距离，check下

from sklearn.neighbors import

nbrs = NearestNeighbors(n_neighbors=len(iris_df), n_jobs=-1)nbrs.fit(iris_df.iloc[:,[2,3]])

NearestNeighbors(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=-1, n_neighbors=150, p=2, radius=1.0)

d, index = nbrs.kneighbors(iris_df.iloc[:,[2,3]],return_distance=True)

from functools import reduce #python 3total_distance = reduce(lambda x,y: x+y,np.array(pd.DataFrame(d).iloc[:,1:150]).tolist())

from scipy import

stats.describe(total_distance)

DescribeResult(nobs=22350, minmax=(0.0, 6.262587324740471), mean=2.185682454621745, variance=2.6174775533104904, skewness=0.3422940721262964, kurtosis=-1.1637573960810108)

pd.DataFrame({"total_distance":total_distance}).describe()

从数据上看，有点接近25%分位数。

meanshift的简单介绍到此为止，有些业务场景下，这个算法还是很好用的。需要具体问题具体分析。

2021-03-31 于南京市江宁区九龙湖

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