How to use Pearson Correlation as distance metric in Scikit-learn Agglomerative clustering

Question

I have the following data:

State   Murder  Assault UrbanPop    Rape
Alabama 13.200  236 58  21.200
Alaska  10.000  263 48  44.500
Arizona 8.100   294 80  31.000
Arkansas    8.800   190 50  19.500
California  9.000   276 91  40.600
Colorado    7.900   204 78  38.700
Connecticut 3.300   110 77  11.100
Delaware    5.900   238 72  15.800
Florida 15.400  335 80  31.900
Georgia 17.400  211 60  25.800
Hawaii  5.300   46  83  20.200
Idaho   2.600   120 54  14.200
Illinois    10.400  249 83  24.000
Indiana 7.200   113 65  21.000
Iowa    2.200   56  57  11.300
Kansas  6.000   115 66  18.000
Kentucky    9.700   109 52  16.300
Louisiana   15.400  249 66  22.200
Maine   2.100   83  51  7.800
Maryland    11.300  300 67  27.800
Massachusetts   4.400   149 85  16.300
Michigan    12.100  255 74  35.100
Minnesota   2.700   72  66  14.900
Mississippi 16.100  259 44  17.100
Missouri    9.000   178 70  28.200
Montana 6.000   109 53  16.400
Nebraska    4.300   102 62  16.500
Nevada  12.200  252 81  46.000
New Hampshire   2.100   57  56  9.500
New Jersey  7.400   159 89  18.800
New Mexico  11.400  285 70  32.100
New York    11.100  254 86  26.100
North Carolina  13.000  337 45  16.100
North Dakota    0.800   45  44  7.300
Ohio    7.300   120 75  21.400
Oklahoma    6.600   151 68  20.000
Oregon  4.900   159 67  29.300
Pennsylvania    6.300   106 72  14.900
Rhode Island    3.400   174 87  8.300
South Carolina  14.400  279 48  22.500
South Dakota    3.800   86  45  12.800
Tennessee   13.200  188 59  26.900
Texas   12.700  201 80  25.500
Utah    3.200   120 80  22.900
Vermont 2.200   48  32  11.200
Virginia    8.500   156 63  20.700
Washington  4.000   145 73  26.200
West Virginia   5.700   81  39  9.300
Wisconsin   2.600   53  66  10.800
Wyoming 6.800   161 60  15.600

Which I use to perform the hierarchical clustering based on the state. This is the full working code:

import pandas as pd 
from sklearn.cluster import AgglomerativeClustering
df = pd.io.parsers.read_table("http://dpaste.com/031VZPM.txt")
samples = df["State"].tolist()
ndf = df[["Murder", "Assault", "UrbanPop","Rape"]]
X = ndf.as_matrix()

cluster = AgglomerativeClustering(n_clusters=3, 
                               linkage='complete',affinity='euclidean').fit(X)
label = cluster.labels_
outclust = list(zip(label, samples))  
outclust_df = pd.DataFrame(outclust,columns=["Clusters","Samples"])  

for clust in outclust_df.groupby("Clusters"):
    print (clust)

Notice that in that method I use euclidean distance. What I want to do is to use 1-Pearson correlation distance. In R it looks like this:

dat <- read.table("http://dpaste.com/031VZPM.txt",sep="\t",header=TRUE)
dist2 = function(x) as.dist(1-cor(t(x), method="pearson"))
dat = dat[c("Murder","Assault","UrbanPop","Rape")]
hclust(dist2(dat), method="ward.D")

How can I achieve that using Scikit-learn AgglomerativeClustering? I understand that there is the 'precomputed' arguments for affinity. But not sure how to use that to address my problem.

Answer 1

You can define a custom affinity matrix as a function which takes in your data and returns the affinity matrix:

from scipy.stats import pearsonr
import numpy as np

def pearson_affinity(M):
   return 1 - np.array([[pearsonr(a,b)[0] for a in M] for b in M])

Then you can call the agglomerative clustering with this as the affinity function (you have to change the linkage, since 'ward' only works for euclidean distance.

cluster = AgglomerativeClustering(n_clusters=3, linkage='average',
                           affinity=pearson_affinity)
cluster.fit(X)

Note that it doesn't seem to work very well for your data for some reason:

cluster.labels_
Out[107]: 
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,
       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 1, 0])