python - 如何可视化交易矩阵

Question

这就是事务矩阵（数据框）的样子：

{'Avg. Winter temp 0-10C': {0: 1.0, 1: 1.0},
 'Avg. Winter temp < 0C': {0: 0.0, 1: 0.0},
 'Avg. Winter temp > 10C': {0: 0.0, 1: 0.0},
 'Avg. summer temp 11-20C': {0: 0.0, 1: 1.0},
 'Avg. summer temp 20-25C': {0: 1.0, 1: 0.0},
 'Avg. summer temp > 25C': {0: 0.0, 1: 0.0},
 'GENDER_DESC:F': {0: 0.0, 1: 1.0},
 'GENDER_DESC:M': {0: 1.0, 1: 0.0},
 'MODEL_TYPE:FED EMP': {0: 0.0, 1: 0.0},
 'MODEL_TYPE:HCPROV': {0: 0.0, 1: 0.0},
 'MODEL_TYPE:IPA': {0: 0.0, 1: 0.0},
 'MODEL_TYPE:MED A': {0: 0.0, 1: 0.0},
 'MODEL_TYPE:MED ADVG': {0: 0.0, 1: 0.0},
 'MODEL_TYPE:MED B': {0: 1.0, 1: 0.0},
 'MODEL_TYPE:MED SNPG': {0: 0.0, 1: 0.0},
 'MODEL_TYPE:MED UNSP': {0: 0.0, 1: 1.0},
 'MODEL_TYPE:MEDICAID': {0: 0.0, 1: 0.0},
 'MODEL_TYPE:MEDICARE': {0: 0.0, 1: 0.0},
 'MODEL_TYPE:PPO': {0: 0.0, 1: 0.0},
 'MODEL_TYPE:TPA': {0: 0.0, 1: 0.0},
 'MODEL_TYPE:UNSPEC': {0: 0.0, 1: 0.0},
 'MODEL_TYPE:WORK COMP': {0: 0.0, 1: 0.0},
 'Multiple_Cancer_Flag:No': {0: 1.0, 1: 1.0},
 'Multiple_Cancer_Flag:Yes': {0: 0.0, 1: 0.0},
 'PATIENT_AGE_GROUP 30-65': {0: 0.0, 1: 0.0},
 'PATIENT_AGE_GROUP 65-69': {0: 0.0, 1: 0.0},
 'PATIENT_AGE_GROUP 69-71': {0: 1.0, 1: 0.0},
 'PATIENT_AGE_GROUP 71-77': {0: 0.0, 1: 0.0},
 'PATIENT_AGE_GROUP 77-85': {0: 0.0, 1: 1.0},
 'PATIENT_LOCATION:ARIZONA': {0: 0.0, 1: 0.0},
 'PATIENT_LOCATION:CALIFORNIA': {0: 0.0, 1: 0.0},
 'PATIENT_LOCATION:CONNECTICUT': {0: 0.0, 1: 0.0},
 'PATIENT_LOCATION:DELAWARE': {0: 0.0, 1: 0.0},
 'PATIENT_LOCATION:FLORIDA': {0: 0.0, 1: 0.0},
 'PATIENT_LOCATION:GEORGIA': {0: 0.0, 1: 0.0},
 'PATIENT_LOCATION:IOWA': {0: 0.0, 1: 0.0},
 'PATIENT_LOCATION:KANSAS': {0: 0.0, 1: 0.0},
 'PATIENT_LOCATION:KENTUCKY': {0: 0.0, 1: 0.0},
 'PATIENT_LOCATION:LOUISIANA': {0: 0.0, 1: 0.0},
 'PATIENT_LOCATION:MARYLAND': {0: 0.0, 1: 0.0},
 'PATIENT_LOCATION:MASSACHUSETTS': {0: 0.0, 1: 0.0},
 'PATIENT_LOCATION:MICHIGAN': {0: 0.0, 1: 0.0},
 'PATIENT_LOCATION:MINNESOTA': {0: 0.0, 1: 0.0},
 'PATIENT_LOCATION:MISSISSIPPI': {0: 0.0, 1: 0.0},
 'PATIENT_LOCATION:MISSOURI': {0: 0.0, 1: 0.0},
 'PATIENT_LOCATION:NEBRASKA': {0: 0.0, 1: 0.0},
 'PATIENT_LOCATION:NEW JERSEY': {0: 0.0, 1: 0.0},
 'PATIENT_LOCATION:NEW MEXICO': {0: 1.0, 1: 0.0},
 'PATIENT_LOCATION:NEW YORK': {0: 0.0, 1: 0.0},
 'PATIENT_LOCATION:OKLAHOMA': {0: 0.0, 1: 0.0},
 'PATIENT_LOCATION:OREGON': {0: 0.0, 1: 0.0},
 'PATIENT_LOCATION:PENNSYLVANIA': {0: 0.0, 1: 0.0},
 'PATIENT_LOCATION:SOUTH CAROLINA': {0: 0.0, 1: 0.0},
 'PATIENT_LOCATION:TENNESSEE': {0: 0.0, 1: 0.0},
 'PATIENT_LOCATION:TEXAS': {0: 0.0, 1: 0.0},
 'PATIENT_LOCATION:VIRGINIA': {0: 0.0, 1: 0.0},
 'PATIENT_LOCATION:WASHINGTON': {0: 0.0, 1: 1.0},
 'PAYER_TYPE:Commercial': {0: 0.0, 1: 0.0},
 'PAYER_TYPE:Managed Medicaid': {0: 0.0, 1: 0.0},
 'PAYER_TYPE:Medicare': {0: 1.0, 1: 0.0},
 'PAYER_TYPE:Medicare D': {0: 0.0, 1: 1.0},
 'PLAN_NAME:ALL OTHER THIRD PARTY': {0: 0.0, 1: 0.0},
 'PLAN_NAME:BCBS FL UNSPECIFIED': {0: 0.0, 1: 0.0},
 'PLAN_NAME:BCBS MI MEDICARE D GENERAL (MI)': {0: 0.0, 1: 0.0},
 'PLAN_NAME:BCBS TEXAS GENERAL (TX)': {0: 0.0, 1: 0.0},
 'PLAN_NAME:BLUE CARE (MS)': {0: 0.0, 1: 0.0},
 'PLAN_NAME:BLUE PREFERRED PPO (AZ)': {0: 0.0, 1: 0.0},
 'PLAN_NAME:CMMNWLTH CRE MED SNP GENERAL(MA)': {0: 0.0, 1: 0.0},
 'PLAN_NAME:DEPT OF VETERANS AFFAIRS': {0: 0.0, 1: 0.0},
 'PLAN_NAME:EMBLEMHEALTH/HIP/GHI UNSPEC': {0: 0.0, 1: 0.0},
 'PLAN_NAME:ESSENCE MED ADV GENERAL (MO)': {0: 0.0, 1: 0.0},
 'PLAN_NAME:HEALTH NET MED D GENERAL (OR)': {0: 0.0, 1: 0.0},
 'PLAN_NAME:HIGHMARK UNSPECIFIED': {0: 0.0, 1: 0.0},
 'PLAN_NAME:HUMANA MED D GENERAL(MN)': {0: 0.0, 1: 0.0},
 'PLAN_NAME:HUMANA-UNSPECIFIED': {0: 0.0, 1: 0.0},
 'PLAN_NAME:KEYSTONE FIRST (PA)': {0: 0.0, 1: 0.0},
 'PLAN_NAME:MEDICARE A': {0: 0.0, 1: 0.0},
 'PLAN_NAME:MEDICARE A KENTUCKY (KY)': {0: 0.0, 1: 0.0},
 'PLAN_NAME:MEDICARE A MINNESOTA (MN)': {0: 0.0, 1: 0.0},
 'PLAN_NAME:MEDICARE B': {0: 0.0, 1: 0.0},
 'PLAN_NAME:MEDICARE B ARIZONA (AZ)': {0: 0.0, 1: 0.0},
 'PLAN_NAME:MEDICARE B IOWA (IA)': {0: 0.0, 1: 0.0},
 'PLAN_NAME:MEDICARE B KANSAS (KS)': {0: 0.0, 1: 0.0},
 'PLAN_NAME:MEDICARE B NEW MEXICO (NM)': {0: 1.0, 1: 0.0},
 'PLAN_NAME:MEDICARE B PENNSYLVANIA (PA)': {0: 0.0, 1: 0.0},
 'PLAN_NAME:MEDICARE B TEXAS (TX)': {0: 0.0, 1: 0.0},
 'PLAN_NAME:MEDICARE B VIRGINIA (VA)': {0: 0.0, 1: 0.0},
 'PLAN_NAME:MEDICARE UNSP': {0: 0.0, 1: 0.0},
 'PLAN_NAME:MOLINA HEALTHCARE (FL)': {0: 0.0, 1: 0.0},
 'PLAN_NAME:OPTUMHEALTH PHYSICAL HEALTH': {0: 0.0, 1: 0.0},
 'PLAN_NAME:PACIFICSOURCE HP MED ADV GNRL': {0: 0.0, 1: 0.0},
 'PLAN_NAME:PAI PLANNED ADMIN INC (SC)': {0: 0.0, 1: 0.0},
 'PLAN_NAME:PEOPLES HLTH NETWORK': {0: 0.0, 1: 0.0},
 'PLAN_NAME:THE COVENTRY CORP UNSPECIFIED': {0: 0.0, 1: 0.0},
 'PLAN_NAME:UHC/PAC/AARP MED D GENERAL (FL)': {0: 0.0, 1: 0.0},
 'PLAN_NAME:UHC/PAC/AARP MED D GENERAL (MD)': {0: 0.0, 1: 0.0},
 'PLAN_NAME:UHC/PAC/AARP MED D GENERAL (NY)': {0: 0.0, 1: 0.0},
 'PLAN_NAME:UHC/PAC/AARP MED D GENERAL (TX)': {0: 0.0, 1: 0.0},
 'PLAN_NAME:UHC/PAC/AARP MED D GENERAL (WA)': {0: 0.0, 1: 1.0},
 'PLAN_NAME:UNITED HLTHCARE-(CT) CT PPO': {0: 0.0, 1: 0.0},
 'PLAN_NAME:UNITED HLTHCARE-(NE) MIDLANDS': {0: 0.0, 1: 0.0},
 'PLAN_NAME:UNITED HLTHCARE-UNSPECIFIED': {0: 0.0, 1: 0.0},
 'PLAN_NAME:UNITED MEDICAL RESOURCES/UMR': {0: 0.0, 1: 0.0},
 'PLAN_NAME:WORKERS COMP - EMPLOYER': {0: 0.0, 1: 0.0},
 'PRI_SPECIALTY_DESC:DERMATOLOGY': {0: 0.0, 1: 0.0},
 'PRI_SPECIALTY_DESC:HEMATOLOGY/ONCOLOGY': {0: 1.0, 1: 0.0},
 'PRI_SPECIALTY_DESC:INTERNAL MEDICINE': {0: 0.0, 1: 0.0},
 'PRI_SPECIALTY_DESC:MEDICAL ONCOLOGY': {0: 0.0, 1: 1.0},
 'PRI_SPECIALTY_DESC:NURSE PRACTITIONER': {0: 0.0, 1: 0.0},
 'PRI_SPECIALTY_DESC:OBSTETRICS & GYNECOLOGY': {0: 0.0, 1: 0.0},
 'PROVIDER_LOCATION:ARIZONA': {0: 0.0, 1: 0.0},
 'PROVIDER_LOCATION:CALIFORNIA': {0: 0.0, 1: 0.0},
 'PROVIDER_LOCATION:CONNECTICUT': {0: 0.0, 1: 0.0},
 'PROVIDER_LOCATION:DELAWARE': {0: 0.0, 1: 0.0},
 'PROVIDER_LOCATION:FLORIDA': {0: 0.0, 1: 0.0},
 'PROVIDER_LOCATION:IOWA': {0: 0.0, 1: 0.0},
 'PROVIDER_LOCATION:KANSAS': {0: 0.0, 1: 0.0},
 'PROVIDER_LOCATION:KENTUCKY': {0: 0.0, 1: 0.0},
 'PROVIDER_LOCATION:LOUISIANA': {0: 0.0, 1: 0.0},
 'PROVIDER_LOCATION:MASSACHUSETTS': {0: 0.0, 1: 0.0},
 'PROVIDER_LOCATION:MICHIGAN': {0: 0.0, 1: 0.0},
 'PROVIDER_LOCATION:MINNESOTA': {0: 0.0, 1: 0.0},
 'PROVIDER_LOCATION:MISSISSIPPI': {0: 0.0, 1: 0.0},
 'PROVIDER_LOCATION:MISSOURI': {0: 0.0, 1: 0.0},
 'PROVIDER_LOCATION:NEBRASKA': {0: 0.0, 1: 0.0},
 'PROVIDER_LOCATION:NEW MEXICO': {0: 1.0, 1: 0.0},
 'PROVIDER_LOCATION:NEW YORK': {0: 0.0, 1: 0.0},
 'PROVIDER_LOCATION:OREGON': {0: 0.0, 1: 0.0},
 'PROVIDER_LOCATION:PENNSYLVANIA': {0: 0.0, 1: 0.0},
 'PROVIDER_LOCATION:SOUTH CAROLINA': {0: 0.0, 1: 0.0},
 'PROVIDER_LOCATION:TENNESSEE': {0: 0.0, 1: 0.0},
 'PROVIDER_LOCATION:TEXAS': {0: 0.0, 1: 0.0},
 'PROVIDER_LOCATION:VIRGINIA': {0: 0.0, 1: 0.0},
 'PROVIDER_LOCATION:WASHINGTON': {0: 0.0, 1: 1.0},
 'PROVIDER_TYP_DESC:PROFESSIONAL': {0: 1.0, 1: 1.0},
 'Region:MIDWEST': {0: 0.0, 1: 0.0},
 'Region:NORTHEAST': {0: 0.0, 1: 0.0},
 'Region:SOUTH': {0: 0.0, 1: 0.0},
 'Region:WEST': {0: 1.0, 1: 1.0},
 'Vials Consumption == 1': {0: 0.0, 1: 0.0},
 'Vials_Consumption_GROUP 1-2': {0: 0.0, 1: 0.0},
 'Vials_Consumption_GROUP 12-91': {0: 0.0, 1: 0.0},
 'Vials_Consumption_GROUP 2-3': {0: 0.0, 1: 0.0},
 'Vials_Consumption_GROUP 3-6': {0: 0.0, 1: 1.0},
 'Vials_Consumption_GROUP 6-12': {0: 1.0, 1: 0.0},
 'keytruda_flag:No': {0: 1.0, 1: 1.0},
 'keytruda_flag:Yes': {0: 0.0, 1: 0.0},
 'libtayo_flag:No': {0: 0.0, 1: 0.0},
 'libtayo_flag:Yes': {0: 1.0, 1: 1.0},
 'optivo_flag:No': {0: 1.0, 1: 1.0},
 'optivo_flag:Yes': {0: 0.0, 1:

 0.0}}

这是一个交易矩阵。使用以下方法创建规则：

from mlxtend.frequent_patterns import apriori
frequent_itemsets = apriori(train_bucket, min_support=0.2, use_colnames=True)
print (frequent_itemsets)

并使用此创建规则：

from mlxtend.frequent_patterns import association_rules
association_rules(frequent_itemsets, metric="confidence", min_threshold=0.7)
rules = association_rules(frequent_itemsets, metric="lift", min_threshold=1.2)
print (len(rules["antecedents"]))

它给出了 10k 条规则。我需要能够可视化这些。我尝试使用这个： https ://intelligentonlinetools.com/blog/2018/02/10/how-to-create-data-visualization-for-association-rules-in-data-mining/

我尝试了networkX示例，它给出了这个：

如果我全部绘制，它会变得混乱。

我曾想过应用 t-SNE，但这在初始交易矩阵上使用起来没有多大意义。试过这样

import numpy as np
from sklearn.manifold import TSNE
X = train_bucket
X_embedded = TSNE(n_components=2).fit_transform(X)
X_embedded.shape
from sklearn.manifold import TSNE
from matplotlib import pyplot as plt
import seaborn as sns
sns.set(rc={'figure.figsize':(11.7,8.27)})
palette = sns.color_palette("bright", 10)
sns.scatterplot(X_embedded[:,0], X_embedded[:,1], legend='full', palette=palette)

我不知道如何理解它。我可以探索哪些选项？

标签： pythonnetworkxrules