copy page_ranker to infra

Author: mzargham

infra/page_ranker.py

@@ -0,0 +1,208 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Mar 14 20:43:21 2019
+
+@author: Zargham
+"""
+
+import networkx as nx
+import pandas as pd
+import numpy as np
+
+#defaults
+default_self_loop_wt= .001 
+
+def update_score(g,alpha,seed, lazy=False, lazy_wt = .5):
+    
+    #lazy random walk assumes a topology independent 1/2 wt on self-loops
+    lazy_wt = lazy_wt*float(lazy) 
+    
+    prior_x = nx.get_node_attributes(g,'score')
+    for n in g.nodes:
+        self_wt = g.nodes[n]['self_wt']/g.nodes[n]['total_wt']
+        
+        val = (1-alpha)*self_wt*prior_x[n] + alpha*seed[n]
+        for nb in g.nodes[n]['out_nbr']:
+            #outbound neighbor
+            e_count = edge_count(g, n,nb)
+            for e3 in range(e_count):
+                wt = g.edges[(n,nb,e3)]['out_weight']/g.nodes[nb]['total_wt']
+                val = val + (1-alpha)*wt*prior_x[nb]
+        
+        for nb in g.nodes[n]['in_nbr']:
+            #inbound neighbor
+            e_count = edge_count(g, nb,n)
+            for e3 in range(e_count):
+                wt = g.edges[(nb,n,e3)]['in_weight']/g.nodes[nb]['total_wt']
+                val = val + (1-alpha)*wt*prior_x[nb]
+                    
+        #print(val)
+                    
+        g.nodes[n]['score']= lazy_wt*prior_x[n]+(1-lazy_wt)*val
+    
+    return g
+
+#helper function
+def edge_count(g,src,dst):
+    i =0
+    stop = False
+    while not(stop):
+        try:
+            g.edges[(src,dst,i)]
+            i=i+1
+        except:
+            stop = True
+            return i
+
+#tuples are (to_weight, from_weight)
+default_edge_wt_by_type = {
+    'github/authors': (0.5,1),
+    'github/hasParent':(1,1/4),
+    'git/hasParent':(1,1/4),
+    'github/mentionsAuthor': (1,1/32),
+    'github/mergedAs':(.5,1),
+    'github/references':(1,1/16),
+    'github/reactsHeart':(2,1/32),
+    'github/reactsHooray':(4,1/32),
+    'github/reactsRocket':(1,0), #appears to be missing from current implementation
+    'github/reactsThumbsUp':(1,1/32)
+    }
+
+default_node_wt_by_type = {
+    'github/issue':2.0, 
+    'github/repo':4.0, 
+    'github/comment': 1.0, 
+    'git/commit':2.0, 
+    'github/user':1.0,
+    'github/bot':1.0,
+    'github/review': 1.0, 
+    'github/pull': 4.0
+    }
+
+
+def wt_heuristic(g,
+                 node_wt_by_type=default_node_wt_by_type,
+                 edge_wt_by_type=default_edge_wt_by_type,
+                 self_loop_wt=default_self_loop_wt):
+    
+    for e in g.edges:
+        e_wts = edge_wt_by_type[g.edges[e]['type']]
+        src_wt = node_wt_by_type[g.nodes[e[0]]['type']]
+        dst_wt = node_wt_by_type[g.nodes[e[1]]['type']]
+        
+        g.edges[e]['in_weight'] = e_wts[0]*dst_wt
+        g.edges[e]['out_weight'] = e_wts[1]*src_wt
+    
+    '''
+    for n in g.nodes:
+        wt = self_loop_wt
+        for nb in nx.all_neighbors(g,n):
+            #outbound neighbor
+            if nb in g.neighbors(n):
+                e_count = edge_count(g,n,nb)
+                for e3 in range(e_count):
+                    wt = wt + g.edges[(n,nb,e3)]['out_weight']
+            #inbound neighbor
+            else:
+                e_count = edge_count(g,nb,n)
+                for e3 in range(e_count):
+                    wt = wt + g.edges[(nb,n,e3)]['in_weight']
+                
+        g.nodes[n]['denominator']=wt
+    '''
+    
+    #create neighborhoods
+    for n in g.nodes:
+        g.nodes[n]['all_nbr']= set(nx.all_neighbors(g,n))
+        g.nodes[n]['in_nbr'] = set()
+        g.nodes[n]['out_nbr'] = set()
+        for nb in g.nodes[n]['all_nbr']:
+            #print((n,nb))
+            try :
+                g.edges[(nb,n,0)]
+                g.nodes[n]['in_nbr'].add(nb)
+            except:
+                pass
+            try :
+                g.edges[(n,nb,0)]
+                g.nodes[n]['out_nbr'].add(nb)
+            except:
+                pass
+    
+    for n in g.nodes:
+        self_wt = self_loop_wt#/g.nodes[n]['denominator']
+        g.nodes[n]['self_wt']=self_wt
+        total_wt = self_wt
+        for nb in g.nodes[n]['out_nbr']:
+            #outbound neighbor
+            e_count = edge_count(g, n,nb)
+            for e3 in range(e_count):
+                wt = g.edges[(n,nb,e3)]['in_weight']#/g.nodes[nb]['denominator']
+                #g.edges[(n,nb,e3)]['normalized_out_wt']=wt
+                total_wt = total_wt+wt
+            
+        for nb in g.nodes[n]['in_nbr']:
+           #inbound neighbor
+            e_count = edge_count(g, nb,n)
+            for e3 in range(e_count):
+                wt = g.edges[(nb,n,e3)]['out_weight']#/g.nodes[nb]['denominator']
+                #g.edges[(nb,n,e3)]['normalized_in_wt']=wt
+                total_wt = total_wt+wt
+        
+        
+        g.nodes[n]['total_wt'] = total_wt
+        
+    return g
+
+def pageRanker(g,
+               alpha,
+               K,
+               seed=None,
+               initial_value = None,
+               lazy=False,
+               lazy_wt = .5,
+               lazy_decay = True,
+               self_loop_wt=default_self_loop_wt,
+               node_wt_by_type =default_node_wt_by_type,
+               edge_wt_by_type=default_edge_wt_by_type):
+    
+    #improve input verification for seed
+    #must be dict keyed to nodes
+    #with non-negative floating point values summing to 1
+    if seed==None:
+        N = len(g.nodes)
+        seed = {n:1.0/N for n in g.nodes}
+    
+    #improve input verification for initial value
+    #must be dict keyed to nodes
+    #with non-negative floating point values summing to 1
+    if initial_value==None:
+        initial_value = seed
+
+    for n in g.nodes:    
+        g.nodes[n]['score'] = initial_value[n]    
+    
+    g = wt_heuristic(g,
+                     node_wt_by_type=node_wt_by_type,
+                     edge_wt_by_type=edge_wt_by_type,
+                     self_loop_wt=self_loop_wt)
+    
+    #print(g.nodes[0])
+    
+    x_dict = {0:initial_value}
+    for k in range(0,K):
+        g = update_score(g,
+                         alpha,
+                         seed,
+                         lazy,
+                         lazy_wt*(1-int(lazy_decay)*k/(k+3)))
+        x_dict[k+1] = nx.get_node_attributes(g,'score')
+    
+    
+    #result in numpy array format
+    pr= np.array(list(x_dict[K].values()))
+    
+    #trajectory in pandas dataframe format
+    df = pd.DataFrame(x_dict).T
+    return pr,df, g