python暗网市场关联规则学习菜篮子分析

暗网市场是一个网上黑市，从2014年1月运行，直到上周的周三，当它突然消失了。几天后，一篇reddit帖子中发布了包含该网站成立以来的每日wget抓取。我对数据集进行了一些分析：

产品可以根据销售者进行分类

在Evolution上，有一些顶级类别（“药品”，“数字商品”，“欺诈相关”等）细分为特定于产品的页面。每个页面包含不同供应商的几个列表。

我根据供应商同现关系在产品之间建立了一个图表，即每个节点对应于一种产品，其边权重由同时出售两种事件产品的供应商数量定义。因此，举例来说，如果有3个供应商同时出售甲斯卡林和4-AcO-DMT，那么我的图在甲斯卡林和4-AcO-DMT节点之间的权重为3。我使用基于随机块模型的分层边缘实现来生成以下Evolution产品网络的可视化：


importimport  pandaspandas  asas  pdpd
 importimport  graph_toolgraph_t  as gt
import graph_tool.draw
import graph_tool.community
import itertools
import collections
import matplotlib
import math
In [2]:
df = pd.read_csv('/home/aahu/Downloads/evolution/evolution/products_vendors.tsv',sep='\t')

#discard meta-categories"
meta_cats = ['Other','Drugs','Guides & Tutorials','Fraud Related',
             'Services','Digital Goods','Electronics', 'Custom Listings']
df = df[df['category'].map(lambda x:x not in meta_cats)]     
In [3]:
#df['count'] = df.groupby(['vendor','category']).transform('count').index

#build graph-tool ids
node_lbs = {}
rev_node_lbs = {}
for idx,vendor in enumerate(df['category'].drop_duplicates()):
    node_lbs[vendor] = idx
    rev_node_lbs[idx] = vendor
df['id'] = df['category'].map(lambda x:node_lbs[x])
In [4]:
edge_list = []
dfg = df.groupby('vendor')
for name,group in dfg:
    ei = itertools.combinations(group['id'].drop_duplicates(),2)
    for e in ei:
        edge_list.append(tuple(sorted(e)))
        
#filter edges by num shared vendors
MIN_SHARED_VENDORS=1
c = collections.Counter(edge_list)
edge_list = [e for e in c if c[e]>=MIN_SHARED_VENDORS]

#build graph
g = gt.Graph(directed=False)
g.add_edge_list(edge_list)
g.vertex_properties['label'] = g.new_vertex_property('string')
for v in g.vertices():
    g.vertex_properties['label'][v] = rev_node_lbs[g.vertex_index[v]]
print('g vert/edges: ',g.num_vertices(), g.num_edges())

#add edge weight property
g.edge_properties['weight'] = g.new_edge_property('double')
g.edge_properties['color'] = g.new_edge_property('vector<double>')
for e in g.edges():
    w = c[tuple(sorted([e.source(),e.target()]))]
    g.edge_properties['weight'][e] = w
    alpha = (float(w)/max(c.values())) + .025
    g.edge_properties['color'][e] = [103/255.0,134/255.0,239/255.0,alpha]   
g vert/edges:  73 2219
In [10]:
state = gt.community.minimize_nested_blockmodel_dl(g,deg_corr=False,
                                                   eweight=g.ep['weight'])
bstack = state.get_bstack()
t = gt.community.get_hierarchy_tree(bstack)[0]
tpos = pos = gt.draw.radial_tree_layout(t, t.vertex(t.num_vertices() - 1), weighted=True)
cts = gt.draw.get_hierarchy_control_points(g, t, tpos,beta=.87)
pos = g.own_property(tpos)
b = bstack[0].vp["b"]

#text rotation
text_rot = g.new_vertex_property('double')
g.vertex_properties['text_rot'] = text_rot
for v in g.vertices():
    if pos[v][0] >0:
        text_rot[v] = math.atan(pos[v][1]/pos[v][0])
    else:
        text_rot[v] = math.pi + math.atan(pos[v][1]/pos[v][0])

print('saving to disk...')
gt.draw.graph_draw(g, pos=pos, vertex_fill_color=b,
            edge_control_points=cts,
            vertex_size=20,
            vertex_text=g.vertex_properties['label'],
            vertex_text_rotation=g.vertex_properties['text_rot'],
            vertex_text_position=1,
            vertex_font_size=20,
            vertex_font_family='mono',
            vertex_anchor=0,
            vertex_color=b,
            vcmap=matplotlib.cm.Spectral,
            ecmap=matplotlib.cm.Spectral,
            edge_color=g.edge_properties['color'],
            bg_color=[0,0,0,1],
            output_size=[1024*2,1024*2],
            output='/home/aahu/Desktop/evo_nvends={0}.png'.format(MIN_SHARED_VENDORS))
saving to disk...

它包含73个节点和2,219个边缘（我在数据中找到了3,785个供应商）。

# coding: utf-8

from bs4 import BeautifulSoup
import re
import pandas as pd
import dateutil
import os

import logging
FORMAT = '%(asctime)-15s %(levelname)-6s %(message)s'
DATE_FORMAT = '%b %d %H:%M:%S'
formatter = logging.Formatter(fmt=FORMAT, datefmt=DATE_FORMAT)
handler = logging.StreamHandler()
handler.setFormatter(formatter)
logger = logging.getLogger(__name__)
logger.addHandler(handler)
logger.setLevel(logging.INFO)

DATA_DIR='/home/aahu/Downloads/evolution/evolution/'

def html_to_df(fname, fdate):
    """
    parse an evolution catergory html file
    must spec file date (it doesn't appear in the html)
    """
    logger.info('processing: {}'.format(fname))
    soup = BeautifulSoup(open(fname))
    profs = soup.find_all(href=re.compile('http://k5zq47j6wd3wdvjq.onion/profile/.*'))
    l = []
    for p in profs:
        if p.text != 'simurgh': # Welcome back simurgh!
            d = {}
            d['vendor'] = p.text.strip()
            d['product'] = p.fetchPrevious()[1].text.strip()
            d['category'] = soup.title.text.strip().split('::')[1].strip()
            d['date'] = fdate
            l.append(d)
    return pd.DataFrame(l)

def catdir_to_df(catdir, fdate):
    fs = os.listdir(catdir)
    fs = map(lambda x: os.path.join(catdir,x),fs)
    l = [html_to_df(f,fdate) for f in fs]
    return pd.concat(l).reindex()

def main():
    for datestr in os.listdir(DATA_DIR):
        d1 = os.path.join(DATA_DIR, datestr)
        fdate = dateutil.parser.parse(datestr)
        catdir = os.path.join(d1,'category')
        if os.path.exists(catdir):
            logger.info(catdir)
            df = catdir_to_df(catdir, fdate)
            outname ='category_df_'+datestr+'.tsv'
            df.to_csv(os.path.join(DATA_DIR,outname),'\t',index=False)


if __name__=='__main__':
    main()

权重较高的边缘绘制得更明亮。节点使用随机块模型进行聚类，并且同一聚类中的节点被分配相同的颜色。图的上半部分（对应于毒品）和下半部分（对应于非毒品，即武器/黑客/信用卡/等）之间有明显的分界。这表明销售毒品的供应商销售非毒品的可能性较小，反之亦然。

91.7％的出售速度和MDMA的供应商也出售了摇头丸

关联规则学习是解决市场篮子分析问题的一种直接且流行的方法。传统的应用是根据其他顾客的购物车向购物者推荐商品。由于某些原因，典型的例子是“购买尿布的顾客也购买啤酒”。

我们没有来自Evolution上公开帖子的抓取的客户数据。但是，我们确实拥有每个供应商所销售产品的数据，可以帮助我们量化上述视觉分析所建议的结果。

这是我们的数据库的示例（完整的文件有3,785行（每个供应商一个））：

Vendor	Products
MrHolland	[‘Cocaine’, ‘Cannabis’, ‘Stimulants’, ‘Hash’]
Packstation24	[‘Accounts’, ‘Benzos’, ‘IDs & Passports’, ‘SIM Cards’, ‘Fraud’]
Spinifex	[‘Benzos’, ‘Cannabis’, ‘Cocaine’, ‘Stimulants’, ‘Prescription’, ‘Sildenafil Citrate’]
OzVendor	[‘Software’, ‘Erotica’, ‘Dumps’, ‘E-Books’, ‘Fraud’]
OzzyDealsDirect	[‘Cannabis’, ‘Seeds’, ‘MDMA’, ‘Weed’]
TatyThai	[‘Accounts’, ‘Documents & Data’, ‘IDs & Passports’, ‘Paypal’, ‘CC & CVV’]
PEA_King	[‘Mescaline’, ‘Stimulants’, ‘Meth’, ‘Psychedelics’]
PROAMFETAMINE	[‘MDMA’, ‘Speed’, ‘Stimulants’, ‘Ecstasy’, ‘Pills’]
ParrotFish	[‘Weight Loss’, ‘Stimulants’, ‘Prescription’, ‘Ecstasy’]

关联规则挖掘是计算机科学中的一个巨大领域–在过去的二十年中，已经发表了数百篇论文。

我运行的FP-Growth算法的最小允许支持为40，最小允许置信度为0.1。该算法学习了12,364条规则。

规则前项	后项	支持度	置信度
[‘Speed’, ‘MDMA’]	[‘Ecstasy’]	155	0.91716
[‘Ecstasy’, ‘Stimulants’]	[‘MDMA’]	310	0.768
[‘Speed’, ‘Weed’, ‘Stimulants’]	[‘Cannabis’, ‘Ecstasy’]	68	0.623
[‘Fraud’, ‘Hacking’]	[‘Accounts’]	53	0.623
[‘Fraud’, ‘CC & CVV’, ‘Accounts’]	[‘Paypal’]	43	0.492
[‘Documents & Data’]	[‘Accounts’]	139	0.492
[‘Guns’]	[‘Weapons’]	72	0.98
[‘Weapons’]	[‘Guns’]	72	0.40