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pre_rec_evaluation_back_Feb_18.py
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'''
Created on 25 Nov 2015
@author: Administrator
'''
'''
Created on 24 Nov 2015
@author: Administrator
'''
import numpy as np
from sklearn.metrics.cluster import normalized_mutual_info_score
import copy
import time
def purity_score(clusters, classes):
"""
Calculate the purity score for the given cluster assignments and ground truth classes
:param clusters: the cluster assignments array
:type clusters: numpy.array
:param classes: the ground truth classes
:type classes: numpy.array
:returns: the purity score
:rtype: float
"""
A = np.c_[(clusters,classes)]
n_accurate = 0.
for j in np.unique(A[:,0]):
z = A[A[:,0] == j, 1]
x = np.argmax(np.bincount(z))
n_accurate += len(z[z == x])
return n_accurate / A.shape[0]
def ReadInData(filename):
clusters = {}
fin = open(filename, 'r')
for line in fin.readlines():
res = line.strip().split()
if clusters.has_key(int(res[1])):
ids = clusters.get(int(res[1]))
ids.add(int(res[0]))
clusters[int(res[1])] = ids
else:
ids = set()
ids.add(int(res[0]))
clusters[int(res[1])] = ids
fin.close()
return clusters
def Coverage(s1, s2):
if s1.issuperset(s2) and len(s2)>3:
return True
else:
return False
def Compare(c1, c2):
result_lines = []
precision = 0.0
recall = 0.0
delta = 0.7
sample = 0.7
res1 = set()
res2 = set()
for k2, v2 in c2.items():
for k1, v1 in c1.items():
if len(c2)>3:
if Coverage(v1, v2):
res1.add(k1)
if DeltaCoverage(v1, v2, delta, sample):
res2.add(k1)
c1.pop(k1)
# c2.pop(k2)
precision = len(res1) * 1.0 / len(c2)
recall = len(res1) * 1.0 / len(c1)
delta_precison = len(res2) * 1.0 / len(c2)
delta_recall = len(res2) * 1.0 / len(c1)
print (len(res1), len(res2))
print ('Precision: ' + str(len(res1) * 1.0 / len(c2)))
print ('Recall: ' + str(len(res1) * 1.0 / len(c1)))
print ('---------------------------------')
print ('Delta Precision with delta (' + str(delta) + '): ' + str(len(res2) * 1.0 / len(c2)))
print ('Delta Recall with delta (' + str(delta) + '): ' + str(len(res2) * 1.0 / len(c1)))
result_lines.append(['Precison', 'Entire Graph', precision])
result_lines.append(['Recall', 'Entire Graph', recall])
result_lines.append(['delta_precison', 'Entire Graph', delta_precison])
result_lines.append(['delta_recall', 'Entire Graph', delta_recall])
return result_lines
def Compare2(c1, c2, delta, sample):
result_lines = []
precision = 0.0
recall = 0.0
# delta = 0.7
# sample = 0.7
res1 = set()
res2 = set()
for k2, v2 in c2.items():
for k1, v1 in c1.items():
if len(c2)>3:
if Coverage(v1, v2):
res1.add(k1)
if DeltaCoverage(v1, v2, delta, sample):
res2.add(k1)
c1.pop(k1)
c2.pop(k2)
precision = len(res1) * 1.0 / len(c2)
recall = len(res1) * 1.0 / len(c1)
delta_precison = len(res2) * 1.0 / len(c2)
delta_recall = len(res2) * 1.0 / len(c1)
print (len(res1), len(res2))
print ('Precision: ' + str(len(res1) * 1.0 / len(c2)))
print ('Recall: ' + str(len(res1) * 1.0 / len(c1)))
print ('---------------------------------')
print ('Delta Precision with delta (' + str(delta) + '): ' + str(len(res2) * 1.0 / len(c2)))
print ('Delta Recall with delta (' + str(delta) + '): ' + str(len(res2) * 1.0 / len(c1)))
result_lines.append(['Precison', 'Entire Graph', precision])
result_lines.append(['Recall', 'Entire Graph', recall])
result_lines.append(['delta_precison', 'Entire Graph', delta_precison])
result_lines.append(['delta_recall', 'Entire Graph', delta_recall])
return result_lines
def DeltaCoverage(s1, s2, threshold, sample):
# delta = len(s1 & s2) * 1.0 / len(s2 | s1)
# print("intersection of two set is %d" % (len(s1 & s2)))
# print("union of two set is %d" % (len(s1 | s2)))
delta = len(s1 & s2) * 1.0 / len(s2)
delta2 = len(s1 & s2) * 1.0 / (len(s1) * sample)
# if delta>= threshold and delta<= 2-threshold:
# return True
# elif delta2>= threshold and delta2<= 2-threshold:
# return True
# else:
# return False
# if delta>= threshold and delta<= 2-threshold and delta2>= threshold and delta2<= 2-threshold:
# if delta>= threshold and delta2>= threshold:
if delta>= threshold and delta2>= 0.5:
# print("\n\n\nnew original set:delta=%0.2f, delta2=%0.2f\n"%(delta, delta2))
# print(list(s1))
# print("sample set \n")
# print(list(s2))
# time.sleep(5)
return True
else:
return False
def Compare4(s1, s2, deltas, sample):
result_lines = []
precision = 0.0
recall = 0.0
sample_block_num= len(s2)
original_block_num = len(s1)
for delta in deltas:
res1 = set()
res2 = set()
res2.clear()
res1.clear()
c1={}
c2={}
c1=copy.deepcopy(s1)
c2=copy.deepcopy(s2)
# print('c1 length is %d\n' %len(c1))
# print('c2 length is %d\n' %len(c2))
# time.sleep(5)
for k2, v2 in c2.items():
for k1, v1 in c1.items():
if len(v2)>3:
if Coverage(v1, v2):
res1.add(k1)
if DeltaCoverage(v1, v2, float(delta), sample):
res2.add(k1)
c1.pop(k1)
# c2.pop(k2)
delta_precison = len(res2) * 1.0 / sample_block_num
delta_recall = len(res2) * 1.0 / original_block_num
delta_composite = 2.0*delta_precison*delta_recall* 1.0 / (delta_precison+delta_recall)
print (len(res1), len(res2))
print ('---------------------------------')
print ('Delta Precision with delta (' + str(delta) + '): ' + str(delta_precison))
print ('Delta Recall with delta (' + str(delta) + '): ' + str(delta_recall))
print ('Delta F-measure with delta (' + str(delta) + '): ' + str(delta_composite))
result_lines.append([str(delta)+'_precison', 'Entire Graph', delta_precison])
result_lines.append([str(delta)+'_recall', 'Entire Graph', delta_recall])
result_lines.append([str(delta)+'_composite', 'Entire Graph', delta_composite])
precision = len(res1) * 1.0 / sample_block_num
recall = len(res1) * 1.0 / original_block_num
print ('Precision: ' + str(precision))
print ('Recall: ' + str(recall))
result_lines.append(['Precison', 'Entire Graph', precision])
result_lines.append(['Recall', 'Entire Graph', recall])
return result_lines
def Compare3(s1, s2, deltas, sample):
result_lines = []
precision = 0.0
recall = 0.0
sample_block_num= len(s2)
original_block_num = len(s1)
for delta in deltas:
res1 = set()
res2 = set()
res2.clear()
res1.clear()
c1={}
c2={}
c1=copy.deepcopy(s1)
c2=copy.deepcopy(s2)
# print('c1 length is %d\n' %len(c1))
# print('c2 length is %d\n' %len(c2))
# time.sleep(5)
for k2, v2 in c2.items():
for k1, v1 in c1.items():
if len(v2)>3:
if Coverage(v1, v2):
res1.add(k1)
if DeltaCoverage(v1, v2, float(delta), sample):
res2.add(k1)
c1.pop(k1)
# c2.pop(k2)
delta_precison = len(res2) * 1.0 / sample_block_num
delta_recall = len(res2) * 1.0 / original_block_num
# delta_composite = 2.0*delta_precison*delta_recall* 1.0 / (delta_precison+delta_recall)
print (len(res1), len(res2))
print ('---------------------------------')
print ('Delta Precision with delta (' + str(delta) + '): ' + str(delta_precison))
print ('Delta Recall with delta (' + str(delta) + '): ' + str(delta_recall))
# print ('Delta F-measure with delta (' + str(delta) + '): ' + str(delta_composite))
result_lines.append([str(delta)+'_precison', 'Entire Graph', delta_precison])
result_lines.append([str(delta)+'_recall', 'Entire Graph', delta_recall])
# result_lines.append([str(delta)+'_composite', 'Entire Graph', delta_composite])
precision = len(res1) * 1.0 / sample_block_num
recall = len(res1) * 1.0 / original_block_num
print ('Precision: ' + str(precision))
print ('Recall: ' + str(recall))
result_lines.append(['Precison', 'Entire Graph', precision])
result_lines.append(['Recall', 'Entire Graph', recall])
return result_lines
def sort(a):
for k in range(len(a)):
(a[k][0],a[k][1]) = (a[k][1],a[k][0])
a.sort()
for k in range(len(a)):
(a[k][0],a[k][1]) = (a[k][1],a[k][0])
if __name__ == '__main__':
ground = ReadInData('community.dat')
partition = ReadInData('network_metisResult')
#partition = ReadInData('network500/network_StructuralSamplerResult')
print(len(ground), len(partition))
Compare(ground, partition)
# a = [[1,2,4],[6,5,6],[2,5,9]]
# sort(a)
# print(a)
# x = np.array([[1,2,4],[6,5,6],[2,5,9]])
# x[np.argsort(x)]
# print x
comm = np.loadtxt('community.dat',dtype='int32')
myresult = np.loadtxt('network_metisResult',dtype='int32')
myresult_sorted= np.array( sorted(myresult, key=lambda myresult : myresult[0]))
myresult_sampled= np.array(myresult_sorted[:,0])
myresult_label= np.array(myresult_sorted[:,1])
print (len(myresult))
#comm_sorted =np.array( sorted(comm, key=lambda comm : comm[0]))
comm_sorted =np.array( sorted(comm, key=lambda myresult_sorted : myresult_sorted[0]))
a= myresult[:,0]
b=comm_sorted[myresult[:,0],:]
comm_selected= np.array(comm_sorted[myresult[:,0],:])
truelabel_whole= np.array(comm_sorted[:,0])
truelabel= np.array(comm_selected[:,1])
print (len(truelabel))
print (purity_score(myresult_label, truelabel))
print (normalized_mutual_info_score([0, 0, 0, 0], [0, 1, 2, 3]))
print ("resut is ",normalized_mutual_info_score(truelabel.tolist(), myresult_label.tolist()))
#
#
# clus = np.array([1, 4, 4, 4, 4, 4, 3, 3, 2, 2, 3, 1, 1])
# clas = np.array([5, 1, 2, 2, 2, 3, 3, 3, 1, 1, 1, 5, 2])
# print purity_score(clus, clas)
#