Python Clustering Analysis LinkedIn user network

CODE:

#!/usr/bin/python #-*-Coding:utf-8-*-"Created on 2014-8-26@author:guaguastd@name:linkedin_network_clusters.py" ' Import osimport sysimport jsonfrom urllib2 import httperrorfrom cluster import kmeansclustering, centroid# A helper funct Ion to munge data and build up an XML treesys.path.append (Os.path.join (OS.GETCWD (), "E:", "Eclipse", "LinkedIn", "Dfile")) From mykml import createkmlk = 3# get geo codefrom Geo Import Geo_from_bingg = geo_from_bing () # Load the Dataconnections_d ATA = ' E:\eclipse\LinkedIn\dfile\linkedin_connections.json ' out_file = "E:\eclipse\LinkedIn\dfile\linkedin_clusters _KMEANS.KML "# Open up your saved connections with extended profiles information# or fetch them again from LinkedIn if you P Referconnections = json.loads (open (Connections_data). read ()) [' values ']locations = [c[' location '] [' name '] for C in  Connections if C.has_key (' location ')]# Some basic transformstransforms = [(' Greater ', ' "), (' area ', ')]# Step 1-tally The frequency of each locationcoords_freqs = {}for locations:if not C.has_key (' location '): Continue # Avoid unnecessary I/O and geo Reques TS by building-a cache if Coords_freqs.has_key (location): coords_freqs[location][1] + = 1 Continue t Ransformed_location = Transform in transforms:transformed_location = Transformed_location.replace (            *transform) # Handle potential IO errors with a retry pattern ... while true:num_errors = 0                Try:results = G.geocode (transformed_location, exactly_one=false) print results                    Break except Httperror, e:num_errors + = 1 if num_errors >= 3:  Sys.exit () print >> sys.stderr, e print >> sys.stderr, ' encountered An URLLIB2 error. Trying again ... ' If results is none:continue for result in results: # each RE Sultis of the form ("Description", (x, y)) coords_freqs[location] = [result[1], 1] Break # disambiguation Strategy is ' pick first ' # Step 2-build up data structure for converting locations to Kmlexpanded_coords = []for label I n coords_freqs: # Flip Lat/lon for Google Earth ((lat, lon), f) = Coords_freqs[label] Expanded_coords.append ((Lab El, [(Lon, LAT)] * f) # No need to clutter the map with unnecessary placemarks ... kml_items = [{' label ': Label, ' Co  Ords ': '%s,%s '% coords[0]} for (label, coords) in Expanded_coords] # It would also is helpful to include names of your Contacts on the map for item in kml_items:item[' contacts ' = ' \ n '. Join (['%s '%s. '% (c[' firstName '], c[' Lastnam  E ']) for C in connections if C.has_key ("location") and c[' location ' [' Name '] = = item[' label ']] # Step 3-cluster locations and extend the KML data structure with CENTROIDSC1 = kmeansclustering ([COO RDS for (label, coords_lIST) in expanded_coords for coords in coords_list]) Centroids = [{' label ': ' Controid ', ' coords ': '%s,%s  '% centroid (c)} for C in C1.getclusters (K)]kml_items.extend (centroids) # Step 4-create the final KML output and write it to a filekml = CREATEKML (kml_items) f = open (Out_file, ' W ') F.write (KML) f.close () print ' Data written to ' + Out_file

RESULT:

[Location (Beijing, Beijing, China 54m 0.0s N, $23m 0.0s E)] [Location (Beijing, Beijing, China 54m 0.0s N, $23m 0.0s E)] None[location (CA, states 43m 0.0s N, 122 15m 0.0s W)][location (Birmingham, England, and Kingdom) 29m 0.0s N , 1 55m 0.0s W), location (Birmingham, England, Kingdom-27m 0.0s N, 1 43m 0.0s W), location (Birmingham Airport, E Ngland, Kingdom 27m 0.0s N, 1 44m 0.0s W), location (Birmingham business Park, England, Kingdom, 28m 0.0 s N, 1 43m 0.0s W)][location (Birmingham, England, Kingdom 29m 0.0s N, 1 55m 0.0s W), location (Birmingham, Englan D, Kingdom 27m 0.0s N, 1 43m 0.0s W), location (Birmingham Airport, England, Kingdom, 27m 0.0s N, 1 44m 0.0s W), location (Birmingham business Park, England, the Kingdom 28m 0.0s N, 1 43m 0.0s W)][location (China, 33m 0. 0s n, 103 59m 0.0s E)][location (China/33m 0.0s N, 103 59m 0.0s E)][location (Chengdu, Sichuan, China, 40m 0.0s N, 104 5m 0.0s E)][location (Chengdu, Sichuan, China 40m 0.0s N, 104 5m 0.0s E)][location (Xingtai, Hebei, China PNs 4m 0.0s N, 29m 0.0s e) [ Location (Xingtai, Hebei, China PNs 4m 0.0s N, 98 29m 0.0s E)][location (for states, 27m 0.0s N,][locati 57m 0.0s W) On (for states 27m 0.0s N, 98 57m 0.0s W)][location (Foshan, Guangdong, China, 2m 0.0s N, 113 6m 0.0s E)][location (F Oshan, Guangdong, China 2m 0.0s N, 113 6m 0.0s E)]data written to E:\eclipse\LinkedIn\dfile\linkedin_clusters_kmeans.km L

Python Clustering Analysis LinkedIn user network