第14章　基于基站定位数据的商圈分析

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Nov  9 14:26:54 2017

@author: lu
"""

import numpy as np

import matplotlib.pyplot as plt
import pandas as pd
from scipy.cluster.hierarchy import dendrogram, linkage
from sklearn.cluster import AgglomerativeClustering
"""
programmer_1-->获取数据，进行离差标准化
programmer_2-->画谱系聚类图
programmer_3-->层次聚类算法，并且将每类进行可视化操作
"""


def programmer_1():
    filename = "data/business_circle.xls"
    standardizedfile = "tmp/standardized.xls"

    data = pd.read_excel(filename, index_col=u"基站编号")
    data = (data - data.min()) / (data.max() - data.min())
    data = data.reset_index()

    data.to_excel(standardizedfile, index=False)


def programmer_2():
    standardizedfile = "data/standardized.xls"
    data = pd.read_excel(standardizedfile, index_col=u"基站编号")

    Z = linkage(data, method="ward", metric="euclidean")
    P = dendrogram(Z, 0)
    plt.show()

    return P


def programmer_3():

    standardizedfile = "data/standardized.xls"
    k = 3
    data = pd.read_excel(standardizedfile, index_col=u"基站编号")

    # 层次聚类
    model = AgglomerativeClustering(n_clusters=k, linkage="ward")
    model.fit(data)

    # 详细输入原始数据及对应类别
    r = pd.concat([data, pd.Series(model.labels_, index=data.index)], axis=1)
    r.columns = list(data.columns) + [u"聚类类别"]

    # 绘制聚类图，并且用不同样式进行画图
    style = ["ro-", "go-", "bo-"]
    xlabels = [u"工作日人均停留时间", u"凌晨人均停留时间", u"周末人均停留时间", u"日均人流量"]
    pic_output = "tmp/type_"

    for i in range(k):
        plt.figure()
        tmp = r[r[u"聚类类别"] == i].iloc[:, :4]
        for j in range(len(tmp)):
            plt.plot(range(1, 5), tmp.iloc[j], style[i])

        plt.xticks(range(1, 5), xlabels, rotation=20)

        plt.title(u"商圈类别%s" % (i + 1))
        # 调整底部
        plt.subplots_adjust(bottom=0.15)
        plt.savefig(u"%s%s.png" % (pic_output, i + 1))


if __name__ == "__main__":
    # programmer_1()
    # programmer_2()
    # programmer_3()
    pass