python的關聯圖繪制 --- pyecharts

生活中有很多需要用到關聯圖的地方，至少我認為的是這樣的圖：https://www.echartsjs.com/examples/zh/editor.html?c=graph-npm

我是在使用Word2Vec計算關聯詞的余弦距離之后，想要更好的展示出來的時候，遇到的這種情況，就做了下拓展。

畫圖的步驟主要分為：

1. 將距離數據（或者相關數據）讀入；

2. 按照一定的格式和參數將數據保存為json字符串，可參考：https://www.cnblogs.com/qi-yuan-008/p/12561893.html；

3. 根據json串，繪制關聯圖。

具體而言，主要是：

<1>. 首先有一批數據，如圖所示：

<2>. 導入所需要的包

import json
import pandas as pd
import random
import copy

<3>. 產生顏色隨機值的函數

# 隨機顏色
def randomcolor_func():
    color_char = ['1','2','3','4','5','6','7','8','9','A','B','C','D','E','F']
    color_code = ""
    for i in range(6):
        color_code += color_char[random.randint(0,14)] # randint包括前后節點0和14
    return "#"+color_code

<4>. 生成隨機坐標

# 隨機坐標
#生成隨機數，浮點類型
def generate_position(n):
#    n = 10
    for i in range(n):
        x = round(random.uniform(-2000, 2000), 5)  #一定范圍內的隨機數，范圍可變
        y = round(random.uniform(-2000, 2000), 5)  #控制隨機數的精度round(數值，精度)
    return x, y

<5>. 生成json格式的節點數據

def create_json(data, weights):
    # 自定義節點
    address_dict = {"nodes":[], "edges":[]}
    node_dict = {
          "color": "",
          "label": "",
          "attributes": {},
          "y": None,
          "x": None,
          "id": "",
          "size": None
        }
    edge_dict = {
          "sourceID": "",
          "attributes": {},
          "targetID": "",
          "size": None
        }
    
    # 給節點賦值
    for ii in range(len(data)):
        for jj in range(len(data.iloc[ii])):
            # node，"attributes"屬性可自行設置
            node_dict[r"color"] = randomcolor_func()
            node_dict[r"label"] = data.iloc[ii, jj]
            x, y = generate_position(1)
            node_dict[r"y"] = y
            node_dict[r"x"] = x
            node_dict[r"id"] = data.iloc[ii, jj]
            node_dict[r"size"] = int(weights.loc[data.iloc[ii, jj]])
            
            tmp_node = copy.deepcopy(node_dict)
            address_dict[r"nodes"].append(tmp_node)
            
    for ii in range(len(data)):
        for jj in range(1, len(data.iloc[ii])):       
            # edge
            edge_dict[r"sourceID"] = data.iloc[ii, 0]
            edge_dict[r"targetID"] = data.iloc[ii, jj]
            edge_dict[r"size"] = 2
            
            tmp_edge = copy.deepcopy(edge_dict)
            address_dict["edges"].append(tmp_edge)
    
    return address_dict

<6>. 主函數生成json數據

if __name__ == '__main__': 
    # read data
    data = pd.read_excel(r'test_josn_data.xlsx', 0)
    
    weights = pd.DataFrame({"詞頻":[100, 40, 30, 20, 90, 50, 35, 14, 85, 38, 29, 10]}, 
                            index = ['球類','籃球','足球','羽毛球','美食','肯德基','火鍋','烤魚','飲料','可樂','紅茶','奶茶'])  #建立索引權值列表
    
    address_dict = create_json(data, weights)
    
    with open("write_json.json", "w", encoding='utf-8') as f:
        # json.dump(dict_, f)  # 寫為一行
        json.dump(address_dict, f, indent=2, ensure_ascii=False)  # 寫為多行

最后形成的json數據如下（下載地址）：

<7>. 繪制關聯圖，里面的文件讀取和保存地址自行修改，write_json.json 就是上面保存的json文件

import pyecharts.options as opts
from pyecharts.charts import Graph
import json

with open(r"D:\Python_workspace\spyder_space\test_各種功能\write_json.json", encoding='utf-8') as f:  #設置以utf-8解碼模式讀取文件，encoding參數必須設置，否則默認以gbk模式讀取文件，當文件中包含中文時，會報錯
    data = json.load(f)
#print(data)

nodes = [
    {
        "x": node["x"],
        "y": node["y"],
        "id": node["id"],
        "name": node["label"],
        "symbolSize": node["size"],
        "itemStyle": {"normal": {"color": node["color"]}},
    }
    for node in data["nodes"]
]

edges = [{"source": edge["sourceID"], "target": edge["targetID"]} for edge in data["edges"]]


(
    Graph(init_opts=opts.InitOpts(width="1600px", height="800px"))
    .add(
        series_name="",
        nodes=nodes,
        links=edges,
        layout="none",
        is_roam=True,
        is_focusnode=True,
        label_opts=opts.LabelOpts(is_show=True),
        linestyle_opts=opts.LineStyleOpts(width=0.5, curve=0.3, opacity=0.7),
    )
    .set_global_opts(title_opts=opts.TitleOpts(title="熱詞對應的關聯詞"))
    .render("關聯詞圖.html")
)

最后，就生成了最開始的那張圖。

 

##

參考：

https://www.echartsjs.com/examples/zh/editor.html?c=graph-npm

https://www.cnblogs.com/tester-go/p/7718910.html

https://blog.csdn.net/u014662865/article/details/82016609