def solvePageRank(self, eps, sortRes=False, saveVals=True):
#DO NOT MODIFY THIS FUNCTION -add any extra functions you want to use
#in solveRankToEps
self.rankVec = self.solveRankToEps(eps)
if (len(self.rankVec) > 0):
if (sortRes):
#when converges, to sort self.rankVec also need to sort self.nodeIDs
#so that rankvec is descending and node ids still aligns with it
sortedIDXs, self.rankVec = util.getSortResIDXs(self.rankVec)
newIDVec = [self.nodeIDs[i] for i in sortedIDXs]
self.nodeIDs = newIDVec
else:
#don't save node id list if rank vec not sorted - node ids are
#just idxs in order
newIDVec = None
else:
print('Zero-size PageRank vector Error.')
newIDVec = None
if (saveVals):
print('Saving results')
util.saveRankData(self.outFileName, newIDVec, self.rankVec)
print('Results saved')
return self.rankVec, self.nodeIDs
def plotRes(prObj):
#for graphs you may wish to code
#import matplotlib as plt
import matplotlib.pyplot as plt
#for plotting
import numpy as np
stSiteLoc = 0
endSiteLoc = 20
# use results for .75, .85 and .95 alphas to build plots - vNodeIDxx and
# vRankVecXX results come back sorted in descending rank order
vNodeID75, vRankVec75, dictRV75 = util.getResForPlots(prObj, .75)
vNodeID85, vRankVec85, dictRV85 = util.getResForPlots(prObj, .85)
vNodeID95, vRankVec95, dictRV95 = util.getResForPlots(prObj, .95)
#find union of all top x sites' site ids
allNodesSet = set()
allNodesSet.update(vNodeID75[stSiteLoc:endSiteLoc])
allNodesSet.update(vNodeID85[stSiteLoc:endSiteLoc])
allNodesSet.update(vNodeID95[stSiteLoc:endSiteLoc])
#all nodes in top 20 for any of the 3 alpha settings - unsorted
allTopNodes = list(allNodesSet)
#list of values in allTopNodes order
pltPRVals85tmp = [dictRV85[x] for x in allTopNodes]
#find appropriate order - use idxs to find actual node IDS in allTopNodes
srtdIDXsInATNlst, pltPRVals85 = util.getSortResIDXs(pltPRVals85tmp)
allTopNodes2 = [allTopNodes[x] for x in srtdIDXsInATNlst]
#comparing alpha of .75 and .95 to alpha of .85
pltPRVals75 = [dictRV75[x] for x in allTopNodes2]
pltPRVals95 = [dictRV95[x] for x in allTopNodes2]
# data to plot
n_groups = len(allTopNodes2)
# create plot
fig, ax = plt.subplots()
#index = np.asarray(allTopNodes)#np.arange(n_groups)
index = np.arange(n_groups)
bar_width = 0.30
opacity = 0.8
r1 = plt.bar(index,
pltPRVals75,
bar_width,
alpha=opacity,
color='b',
label='alpha=.75')
r2 = plt.bar(index + bar_width,
pltPRVals85,
bar_width,
alpha=opacity,
color='r',
label='alpha=.85')
r3 = plt.bar(index + 2 * bar_width,
pltPRVals95,
bar_width,
alpha=opacity,
color='g',
label='alpha=.95')
plt.xlabel('Site Rank')
plt.ylabel('Score')
ttlStr = 'Scores for dataset {} for sites ranked {} through {}\n'.format(prObj.inFileName,(stSiteLoc+1),endSiteLoc) \
+ 'for 3 alpha values using {} sink handling'.format('self-loop' if prObj.selfLoops else 'Type 3')
plt.title(ttlStr)
plt.legend()
plt.tight_layout()
plt.show()
