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article_recommend.py
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article_recommend.py
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# coding: utf-8
"""
微信推荐文章
"""
import argparse
import json
import pickle
from collections import Counter
import requests
parser = argparse.ArgumentParser(description="wechat article recommend analysis")
parser.add_argument("--url", type=str, help="wechat article url")
parser.add_argument("--nickname", type=str, help="wechat officename")
parser.add_argument(
"--max_recursive", type=int, default=10, help="recommend url recursive depth"
)
parser.add_argument(
"--high_frequency_value", type=int, default=20, help="max officename frequency"
)
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.125 Safari/537.36"
}
relatedarticle_url = "https://mp.weixin.qq.com/mp/relatedarticle"
def write_data(res_lst, output_fname="graph.html"):
# 绘制graph.html
head = """
<!DOCTYPE html>
<meta charset="utf-8">
<style>
.link {
fill: none;
stroke: #666;
stroke-width: 1.5px;
}
#licensing {
fill: green;
}
.link.licensing {
stroke: green;
}
.link.resolved {
stroke-dasharray: 0, 2 1;
}
circle {
fill: #ccc;
stroke: #333;
stroke-width: 1.5px;
}
text {
font: 12px Microsoft YaHei;
pointer-events: none;
text-shadow: 0 1px 0 #fff, 1px 0 0 #fff, 0 -1px 0 #fff, -1px 0 0 #fff;
}
.linetext {
font-size: 12px Microsoft YaHei;
}
</style>
<body>
<script src="https://d3js.org/d3.v3.min.js"></script>
<script>
var links = [
"""
tail = """
]
var nodes = {};
links.forEach(function (link) {
link.source = nodes[link.source] || (nodes[link.source] = { name: link.source });
link.target = nodes[link.target] || (nodes[link.target] = { name: link.target });
});
var width = 1920, height = 1080;
var force = d3.layout.force()
.nodes(d3.values(nodes))
.links(links)
.size([width, height])
.linkDistance(180)
.charge(-1500)
.on("tick", tick)
.start();
var svg = d3.select("body").append("svg")
.attr("width", width)
.attr("height", height);
var marker =
svg.append("marker")
.attr("id", "resolved")
.attr("markerUnits", "userSpaceOnUse")
.attr("viewBox", "0 -5 10 10")
.attr("refX", 32)
.attr("refY", -1)
.attr("markerWidth", 12)
.attr("markerHeight", 12)
.attr("orient", "auto")
.attr("stroke-width", 2)
.append("path")
.attr("d", "M0,-5L10,0L0,5")
.attr('fill', '#000000');
var edges_line = svg.selectAll(".edgepath")
.data(force.links())
.enter()
.append("path")
.attr({
'd': function (d) { return 'M ' + d.source.x + ' ' + d.source.y + ' L ' + d.target.x + ' ' + d.target.y },
'class': 'edgepath',
'id': function (d, i) { return 'edgepath' + i; }
})
.style("stroke", function (d) {
var lineColor;
lineColor = "#B43232";
return lineColor;
})
.style("pointer-events", "none")
.style("stroke-width", 0.5)
.attr("marker-end", "url(#resolved)");
var edges_text = svg.append("g").selectAll(".edgelabel")
.data(force.links())
.enter()
.append("text")
.style("pointer-events", "none")
.attr({
'class': 'edgelabel',
'id': function (d, i) { return 'edgepath' + i; },
'dx': 80,
'dy': 0
});
edges_text.append('textPath')
.attr('xlink:href', function (d, i) { return '#edgepath' + i })
.style("pointer-events", "none")
.text(function (d) { return d.rela; });
var circle = svg.append("g").selectAll("circle")
.data(force.nodes())
.enter().append("circle")
.style("fill", function (node) {
var color;
var link = links[node.index];
color = "#F9EBF9";
return color;
})
.style('stroke', function (node) {
var color;
var link = links[node.index];
color = "#A254A2";
return color;
})
.attr("r", 28)
.on("click", function (node) {
edges_line.style("stroke-width", function (line) {
console.log(line);
if (line.source.name == node.name || line.target.name == node.name) {
return 4;
} else {
return 0.5;
}
});
})
.call(force.drag);
var text = svg.append("g").selectAll("text")
.data(force.nodes())
.enter()
.append("text")
.attr("dy", ".35em")
.attr("text-anchor", "middle")
.style('fill', function (node) {
var color;
var link = links[node.index];
color = "#A254A2";
return color;
}).attr('x', function (d) {
var re_en = /[a-zA-Z]+/g;
if (d.name.match(re_en)) {
d3.select(this).append('tspan')
.attr('x', 0)
.attr('y', 2)
.text(function () { return d.name; });
}
else if (d.name.length <= 4) {
d3.select(this).append('tspan')
.attr('x', 0)
.attr('y', 2)
.text(function () { return d.name; });
} else {
var top = d.name.substring(0, 4);
var bot = d.name.substring(4, d.name.length);
d3.select(this).text(function () { return ''; });
d3.select(this).append('tspan')
.attr('x', 0)
.attr('y', -7)
.text(function () { return top; });
d3.select(this).append('tspan')
.attr('x', 0)
.attr('y', 10)
.text(function () { return bot; });
}
});
function tick() {
circle.attr("transform", transform1);
text.attr("transform", transform2);
edges_line.attr('d', function (d) {
var path = 'M ' + d.source.x + ' ' + d.source.y + ' L ' + d.target.x + ' ' + d.target.y;
return path;
});
edges_text.attr('transform', function (d, i) {
if (d.target.x < d.source.x) {
bbox = this.getBBox();
rx = bbox.x + bbox.width / 2;
ry = bbox.y + bbox.height / 2;
return 'rotate(180 ' + rx + ' ' + ry + ')';
}
else {
return 'rotate(0)';
}
});
}
function linkArc(d) {
return 'M ' + d.source.x + ' ' + d.source.y + ' L ' + d.target.x + ' ' + d.target.y
}
function transform1(d) {
return "translate(" + d.x + "," + d.y + ")";
}
function transform2(d) {
return "translate(" + (d.x) + "," + d.y + ")";
}
</script>
"""
with open(output_fname, "w", encoding="utf-8") as f:
f.write(head)
for item in res_lst:
if len(item["target"]) < 20:
for target in item["target"]:
f.write(
'{ source: "'
+ item["source"]
+ '", target: "'
+ target
+ '" },'
+ "\n"
)
f.write(tail)
def merge_dict(source_set, lst):
# 合并相同source的target
res_lst = []
for source in source_set:
tmp = list(filter(lambda i: i["source"] == source, lst))
tmp_set = set()
for line in tmp:
tmp_set = tmp_set | line["target"]
res_lst.append({"source": source, "target": tmp_set})
return res_lst
def get_recommend_article(article_url):
params = {
"action": "getlist",
"count": 3, # 可修改
"begin": 0,
"article_url": article_url,
}
res = requests.get(relatedarticle_url, headers=headers, params=params)
return res.json()["list"]
def run(start_article_url, max_recursive=10, fname="d.pkl"):
article_url_lst = [start_article_url]
traverse_url_set = set()
data_lst = []
recursive_count = 0
try:
while recursive_count < max_recursive:
tmp_url_set = set()
# 遍历爬取的文章
for article_url in article_url_lst:
recommend_article_lst = get_recommend_article(article_url)
data_lst.append({article_url: recommend_article_lst}) # 存储已经爬取的文章
traverse_url_set.add(article_url) # 已经爬取过
# 获取新的推荐文章，避免重复用set
for item in recommend_article_lst:
tmp_url_set.add(item["url"])
# 如果已经爬取过，则跳过
article_url_lst = []
for article_url in tmp_url_set:
if article_url not in traverse_url_set:
article_url_lst.append(article_url)
print("第{}层: {}条数据".format(recursive_count, len(article_url_lst)))
recursive_count += 1
except Exception as e:
print(e)
finally:
with open(fname, "wb") as f:
pickle.dump(data_lst, f)
def analysis(url_nickname_dict, fname="d.pkl", high_frequency_value=20):
"""
url_nickname_dict: 链接与公众号名字的k与v
"""
with open(fname, "rb") as f:
data_lst = pickle.load(f)
# 筛选出出现次数最多的公众号
# traverse_url_set = set()
nickname_lst = []
for recommend_d in data_lst:
recommend_item_lst = list(recommend_d.values())[0]
for recommend_item in recommend_item_lst:
# if recommend_item['url'] not in traverse_url_set:
# traverse_url_set.add(recommend_item['url'])
nickname_lst.append(recommend_item["nickname"])
nc = Counter(nickname_lst)
print(nc.most_common()[:high_frequency_value])
high_frequency_nickname_lst = [
k for k, freq in nc.most_common()[:high_frequency_value]
]
# assert 1 == 2
for recommend_d in data_lst:
recommend_item_lst = list(recommend_d.values())[0]
for recommend_item in recommend_item_lst:
url_nickname_dict[recommend_item["url"]] = recommend_item["nickname"]
res_lst = []
for recommend_d in data_lst:
url_k = list(recommend_d.keys())[0]
recomment_lst = recommend_d[url_k]
source_nickname = url_nickname_dict[url_k]
# if source_nickname not in high_frequency_nickname_lst:
# continue
res_lst.append(
{
"source": source_nickname,
"target": {
recomment_item["nickname"]
for recomment_item in recomment_lst
if recomment_item["nickname"] in high_frequency_nickname_lst
},
}
)
source_set = set(map(lambda item: item["source"], res_lst))
res_lst = merge_dict(source_set, res_lst)
write_data(res_lst, fname.split(".pkl")[0] + "_graph.html")
def main():
args = parser.parse_args()
article_url = args.url
nickname = args.nickname
max_recursive = args.max_recursive
high_frequency_value = args.high_frequency_value
fname = "{}_{}.pkl".format(nickname, max_recursive)
run(article_url, max_recursive=max_recursive, fname=fname)
analysis(
{article_url: nickname}, fname=fname, high_frequency_value=high_frequency_value
)
if __name__ == "__main__":
# nickname = '黑马青年'
# article_url = 'https://mp.weixin.qq.com/s?__biz=MzUzNjk1NDIyNg==&mid=2247506248&idx=1&sn=c9a8b4d2e11fd1b6ae924877da3f0ea8&chksm=faeccd55cd9b44432ea81578b1e33db2ae5ce938ed5a90e32847cb0ea88916a16ac47c92e135&scene=132#wechat_redirect'
# nickname = '大渝网'
# article_url = 'https://mp.weixin.qq.com/s/4dm3DLlLjhR9eaK9c_BsBA'
main()