From 992e54bde8d9c017597c178f6ce3bade00e10d85 Mon Sep 17 00:00:00 2001
From: chamalee <chamalee.wickramaarachch@helsinki.fi>
Date: Tue, 18 Jul 2023 03:33:31 +0300
Subject: [PATCH] experiments added: revision
---
.DS_Store | Bin 0 -> 8196 bytes
aff-lam-fixed.py | 275 ++++++++++++++++++++++
lam-aff-2-ld.py => aff-lam-ld.py | 87 +++----
bikes_santander.py | 2 +-
lam-aff-1-fixed.py | 269 ---------------------
lam-aff-1-ld.py | 386 -------------------------------
lam-aff-2-fixed.py | 262 ---------------------
sbm_core.py | 43 ++--
8 files changed, 336 insertions(+), 988 deletions(-)
create mode 100644 .DS_Store
create mode 100644 aff-lam-fixed.py
rename lam-aff-2-ld.py => aff-lam-ld.py (89%)
delete mode 100644 lam-aff-1-fixed.py
delete mode 100644 lam-aff-1-ld.py
delete mode 100644 lam-aff-2-fixed.py
mode change 100755 => 100644 sbm_core.py
diff --git a/.DS_Store b/.DS_Store
new file mode 100644
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diff --git a/aff-lam-fixed.py b/aff-lam-fixed.py
new file mode 100644
index 0000000..c3a7ba8
--- /dev/null
+++ b/aff-lam-fixed.py
@@ -0,0 +1,275 @@
+
+
+"""
+Effect of lambda: Fixed
+Dataset-1
+"""
+import numpy as np
+import pandas as pd
+import utils
+import sbm_core
+import math
+from itertools import combinations
+import itertools
+from sklearn.metrics.cluster import adjusted_rand_score
+
+# Initilaize
+np.random.seed(34573251)
+
+results = np.zeros((50,3) , dtype=float)
+
+for itr_no in range(0,50):
+
+ num_roles=2
+ num_vertices=20
+ num_segments = 4
+
+ NO_SAMPLES= 1850
+ group_assignment= np.random.randint(num_roles, size=(num_vertices))
+
+ nodes = np.arange(num_vertices)
+
+ list_of_groups= [[] for _ in range(num_roles)]
+
+ for idx, val in enumerate(group_assignment):
+ list_of_groups[val].append(nodes[idx])
+
+ # print(list_of_groups)
+
+ size_all_pairs = {}
+ for k in range(0, num_roles):
+ for g in range(k, num_roles):
+ U=list_of_groups[k]
+ W=list_of_groups[g]
+
+ if k == g:
+ size_all_pairs[k,g] = math.comb(len(U), 2)
+ if k != g:
+ size_all_pairs[k,g] = len(U)*len(W)
+
+ lamda_arr = np.ones((num_roles, num_roles,num_segments) , dtype=float)
+ lamda_arr_act = np.zeros((num_roles, num_roles,num_segments) , dtype=float)
+
+ num_levels = 2
+ H =num_levels
+
+ # h-level lambda estimates
+ lambda_estimates_h = np.random.rand(num_roles, num_roles, H)
+
+ # _itr = 8 # Change _itr from 0 to 8 for large lambda differences
+ # _itr = 1
+ # yu = (9-_itr)*.1
+ # lambda_estimates_h[0,0,:] = [yu, 0.01]
+ # lambda_estimates_h[0,1,:] = [0.01, yu]
+ # lambda_estimates_h[1,0,:] = lambda_estimates_h[0,1,:]
+ # lambda_estimates_h[1,1,:] = [yu, yu]
+
+ _itr = 5 # Change _itr from 0 to 8 for smaller lambda differences
+ yu = (9-_itr)*.01
+ lambda_estimates_h[0,0,:] = [yu, 0.01]
+ lambda_estimates_h[0,1,:] = [0.01, yu]
+ lambda_estimates_h[1,0,:] = lambda_estimates_h[0,1,:]
+ lambda_estimates_h[1,1,:] = [yu, yu]
+
+
+ l1 =list(range(0, H))
+ l2 = []
+ if num_segments > num_levels:
+ l2 = [np.random.randint(0,H) for i in range(num_segments-H)]
+
+ # Mapping from segment to a level
+ g_mapping= np.array(l1 + l2)
+ # print('g mapping {}'.format(g_mapping))
+ #Initialize lamda
+ lamda_arr = np.zeros((num_roles, num_roles,num_segments) , dtype=float)
+ for d in range(0, num_segments):
+ lamda_arr[:,:, d]= lambda_estimates_h[:,:,g_mapping[d]]
+
+ change_points_arr = np.zeros((num_roles, num_roles, num_segments+1) , dtype=int)
+ df_all= None
+
+ points= list(range(0, (num_segments+1)*NO_SAMPLES, NO_SAMPLES))
+ list1 = []
+
+ # Generate piecewise non-homogeneous poisson process
+ for k in range(0, num_roles):
+ for g in range(k, num_roles):
+ comb = []
+ if k == g:
+ comb = list(combinations(list_of_groups[k], 2))
+ # print(type(comb))
+ else:
+ # comb = []
+ key_data = [list_of_groups[k],list_of_groups[g],]
+ comb = list(itertools.product(*key_data))
+ # print(comb)
+ if len(comb) != size_all_pairs[k,g]:
+ print('not equal..')
+
+ change_points_arr[k,g,:] = points
+ lamda_arr[k,g,:] = lamda_arr[g,k,:]
+
+ tot_count = np.zeros((num_segments) , dtype=float)
+
+ for pair in comb:
+
+ for d in range(0,num_segments):
+
+ s = np.random.poisson(lamda_arr[k,g,d], NO_SAMPLES)
+ # print(np.count_nonzero(s))
+ tot_count[d] += np.count_nonzero(s)
+
+ list1=[i for i, e in enumerate(s) if e != 0]
+
+ if len(list1) == 0:
+ print('zero')
+
+ list1 = [x+points[d] for x in list1]
+
+ df = pd.DataFrame(data=list1)
+ df.columns =['timestamp']
+
+
+ N= df.size
+
+ list_start_stations =[pair[0]] * N
+ list_end_stations =[pair[1]] * N
+
+ df['source'] = list_start_stations
+ df['target'] = list_end_stations
+
+ df_all=pd.concat([df_all, df], ignore_index=True)
+
+ for d in range(0,num_segments):
+ lamda_arr_act[k,g,d] = tot_count[d]/(NO_SAMPLES*len(comb))
+ # print(tot_count[d])
+ ## Other preparations
+
+ # Remove self loops
+ df_all = df_all[((df_all['source'] ) != (df_all['target']))]
+ #sort
+ df_all=df_all.sort_values('timestamp')
+ df_all = df_all[['target', 'timestamp','source']]
+
+ # Save as .csv file
+ # df_all.to_csv('./Data/synthetic_ground_truth_g1.csv')
+
+ df=df_all
+ dest_folder='./Results/synthetic/3'
+ t_df = df['timestamp']
+
+ nodes_arr = np.union1d(df['target'],df['source']).astype(int)
+ # list of nodes
+ nodes = nodes_arr.tolist()
+ num_vertices = len(nodes)
+
+ # node-group dictionary
+ group_dic = {}
+ keys = nodes
+ values = list(group_assignment)
+ group_dic = dict(zip(keys,values))
+
+
+ # create a new dictionary - key: node-pair , value: list of timestamps
+ dic=df.groupby(['source','target'])['timestamp'].apply(list).to_dict()
+ # print('{} {} {} '.format(group_dic, lamda_arr_act,change_points_arr))
+
+
+ def _swap (row):
+ if row['source'] > row['target']:
+ row['source'] , row['target'] =row['target'] , row['source']
+ return row
+
+ # Undirected graph
+ df=df.apply(lambda row: _swap(row), axis=1)
+ #scale timestamps for zeroth reference point
+ refValue = df['timestamp'].min()
+ df['timestamp'] -= refValue
+
+ # Experiment
+ import experiment
+
+ # User parameters
+ num_roles=2
+ # num_segments=4
+ # num_levels=2# Optional arg
+ algo_ver=3
+ dest_folder='./Results/synthetic/'
+
+ # tuning parameters
+ theta = 1e-7
+ eta = 1
+ tuning_params= {'theta':theta,'eta':eta}
+
+
+ exp_obj = experiment.Experiment(df,num_roles,num_segments,algo_ver,dest_folder,tuning_params,num_levels,refValue)
+ [itr_d,likelihood_d,group_dic_d,lambda_estimates_d,change_points_arr_d] = exp_obj.execute()
+
+
+ t_df = sorted(t_df)
+
+ chg_points = change_points_arr_d[0,0,:]
+ ranges_arr = [ [chg_points[s]+1,chg_points[s+1]] for s in range(0,len(chg_points)-1)]
+ ranges_arr[0][0] = 0
+ list_time_stamps = list(t_df)
+
+
+ # iterate over timestamps list
+ dis_arr = list()
+ gt_arr = list()
+
+
+ for item in list_time_stamps:
+
+ # find the segment which the timestamp belongs
+ # (is dependent on which groups the two nodes belong)
+ d = sbm_core._findSegment(ranges_arr, len(ranges_arr) , int(item))
+ dis_arr.append(d)
+
+
+ chg_points = change_points_arr[0,0,:]
+ ranges_arr = [ [chg_points[s]+1,chg_points[s+1]] for s in range(0,len(chg_points)-1)]
+ ranges_arr[0][0] = 0
+ list_time_stamps = list(t_df)
+
+
+ # iterate over timestamps list
+
+ for item in list_time_stamps:
+
+ # find the segment which the timestamp belongs
+ # (is dependent on which groups the two nodes belong)
+ d = sbm_core._findSegment(ranges_arr, len(ranges_arr) , int(item))
+ gt_arr.append(d)
+
+
+ ind = adjusted_rand_score(gt_arr,dis_arr)
+ # print('rand index: seg {} : {}'.format(_itr, ind))
+
+ g1= group_dic_d
+ g2= group_dic_d[1]
+
+ ds= list(group_dic_d.values() )
+ gt1 = list(g1.values())
+
+ ind_grp=adjusted_rand_score(ds,gt1)
+ # print('rand index: group {} : {}'.format(_itr, ind_grp))
+
+ results[itr_no][0] = ind
+ results[itr_no][1] = itr_d
+ results[itr_no][2] = ind_grp
+
+import pickle
+pickle.dump(results, open('large-fixed-file-{}.pickle'.format(_itr), 'wb'))
+
+arr = results
+ll_avg_val = (sum(arr)/len(arr))
+
+#FINAL RESULTS
+
+print(ll_avg_val)
+print(max(arr[:,0]))
+print(min(arr[:,0]))
+
+print(max(arr[:,1]))
+print(min(arr[:,1]))
diff --git a/lam-aff-2-ld.py b/aff-lam-ld.py
similarity index 89%
rename from lam-aff-2-ld.py
rename to aff-lam-ld.py
index 1d4c07b..deb26c6 100644
--- a/lam-aff-2-ld.py
+++ b/aff-lam-ld.py
@@ -1,10 +1,10 @@
+
"""
Effect of lambda: LD
Dataset-2
"""
-
import numpy as np
import pandas as pd
import utils
@@ -14,19 +14,18 @@ from itertools import combinations
import itertools
from sklearn.metrics.cluster import adjusted_rand_score
# Initilaize
-np.random.seed(3457325)
-
-res = np.zeros((9,5) , dtype=float)
+np.random.seed(34573251)
+results = np.zeros((50,3) , dtype=float)
-for _itr in range(0,1):
+for itr_no in range(0,50):
num_roles=2
num_vertices=20
num_segments = 4
num_levels = 2
- NO_SAMPLES= 200
+ NO_SAMPLES= 1850
nodes = np.arange(num_vertices)
lamda_arr_act = np.zeros((num_roles, num_roles,num_levels) , dtype=float)
@@ -36,12 +35,18 @@ for _itr in range(0,1):
# h-level lambda estimates
lambda_estimates_h = np.random.rand(num_roles, num_roles, H)
- lambda_estimates_h = 1e-2*np.random.randint(11,99, size=(num_roles, num_roles, H))
- # Make high variant lambdas
+ _itr = 3
yu = (9-_itr)*.1
- lambda_estimates_h[0,0,:] = [yu, 0.1]
- lambda_estimates_h[0,1,:] = [0.1, yu]
+ lambda_estimates_h[0,0,:] = [yu, 0.01]
+ lambda_estimates_h[0,1,:] = [0.01, yu]
+ lambda_estimates_h[1,0,:] = lambda_estimates_h[0,1,:]
+ lambda_estimates_h[1,1,:] = [yu, yu]
+
+ _itr = 4
+ yu = (9-_itr)*.01
+ lambda_estimates_h[0,0,:] = [yu, 0.01]
+ lambda_estimates_h[0,1,:] = [0.01, yu]
lambda_estimates_h[1,0,:] = lambda_estimates_h[0,1,:]
lambda_estimates_h[1,1,:] = [yu, yu]
@@ -188,13 +193,10 @@ for _itr in range(0,1):
df_all=pd.concat([df_all, df], ignore_index=True)
- # for dd in level_seg_mapping:
- # dd = d
+
lamda_arr_act[k,g,i] = round(((tot_count[i])/(NO_SAMPLES*com_len[i])),1)
lamda_arr_act[g,k,i] = lamda_arr_act[k,g,i]
- # print('tot count')
- # print(tot_count[dd])
- # print(' {} {} {} {} : k g d :lamb'.format(k,g,d,lamda_arr_act[g,k,dd]))
+
print(' {} {} {} {} : k g d :lamb'.format(k,g,i,lamda_arr_act[g,k,i]))
# Remove self loops
@@ -205,8 +207,7 @@ for _itr in range(0,1):
# Save as .csv file
# df_all.to_csv('./Data/synthetic_ground_truth_g1.csv')
-
-
+
df= None
df=df_all
dest_folder='./Results/synthetic/3'
@@ -296,8 +297,6 @@ for _itr in range(0,1):
ind_seg = adjusted_rand_score(gt_arr,dis_arr)
print('ind {} : {}'.format(_itr, ind_seg))
- liklihood_sum = sbm_core.mm_compute_cost(group_dic,lamda_arr_act,change_points_arr,num_roles,num_segments,dic,g_mapping)
- print(' Initial Actual likelihood .......%f'%liklihood_sum)
print('g mapping {}'.format(g_mapping))
@@ -308,13 +307,12 @@ for _itr in range(0,1):
list_of_groups[val].append(idx)
print('group assignments {}: {}'.format(e_h,list_of_groups))
- #group ass, of level 1
- list_of_groups_1= [[] for _ in range(num_roles)]
- #group ass, of level 2
- list_of_groups_2= [[] for _ in range(num_roles)]
+
g1= group_dic_d[0]
g2= group_dic_d[1]
+ # print('rand index: group {} : {}'.format(_itr, ind_grp))
+
found_cont = 0
for i_h in range(0,num_levels):
@@ -329,8 +327,7 @@ for _itr in range(0,1):
ds= list(group_dic_d[i_h].values() )
gt1 = list(g1.values())
gt2 = list(g2.values())
-
-
+
ind1=adjusted_rand_score(ds,gt1)
ind2=adjusted_rand_score(ds,gt2)
@@ -340,35 +337,25 @@ for _itr in range(0,1):
ind = found_cont/2
- res[_itr][1] = ind
- res[_itr][4] = ind_seg
-print('end')
-
-
-
-# 0.9898951352373943
-# 0.9904822820772498
-# 0.9894069501702982
-# 0.9892811884102554
-# 0.9893223431465236
-# 0.9886669698061425
-# 0.4041524218474172
-# 0.4968349779236352
-# 0.49583738728791915
-
+ results[itr_no][0] = ind_seg
+ results[itr_no][1] = it
+ results[itr_no][2] = ind
+print('end')
+
+import pickle
+# pickle.dump(results, open('max-small-file-{}.pickle'.format(_itr), 'wb'))
+#FINAL RESULTS
-# 3
-# 3
-# 3
-# 6
-# 4
-# 3
-# 5
-# 4
-# 3
+arr = results
+ll_avg_val = (sum(arr)/len(arr))
+print(ll_avg_val)
+print(max(arr[:,0]))
+print(min(arr[:,0]))
+print(max(arr[:,1]))
+print(min(arr[:,1]))
diff --git a/bikes_santander.py b/bikes_santander.py
index 003aadb..ef260be 100755
--- a/bikes_santander.py
+++ b/bikes_santander.py
@@ -62,7 +62,7 @@ df = df[:1000]
num_roles=3
num_segments=7
num_levels=5
-algo_ver= 2
+algo_ver= 3
dest_folder='./Results/bikes/'
# tuning parameters
diff --git a/lam-aff-1-fixed.py b/lam-aff-1-fixed.py
deleted file mode 100644
index 33f914a..0000000
--- a/lam-aff-1-fixed.py
+++ /dev/null
@@ -1,269 +0,0 @@
-
-"""
-Affect of Lambda
-Dataset-1
-"""
-
-import numpy as np
-import pandas as pd
-import utils
-import sbm_core
-import math
-from itertools import combinations
-import itertools
-from sklearn.metrics.cluster import adjusted_rand_score
-
-# Initilaize
-np.random.seed(107)
-
-num_roles=2
-num_vertices=25
-num_segments = 2
-
-NO_SAMPLES= 95
-group_assignment= np.random.randint(num_roles, size=(num_vertices))
-
-nodes = np.arange(num_vertices)
-
-list_of_groups= [[] for _ in range(num_roles)]
-
-for idx, val in enumerate(group_assignment):
- list_of_groups[val].append(nodes[idx])
-
-print(list_of_groups)
-
-size_all_pairs = {}
-for k in range(0, num_roles):
- for g in range(k, num_roles):
- U=list_of_groups[k]
- W=list_of_groups[g]
-
- if k == g:
- size_all_pairs[k,g] = math.comb(len(U), 2)
- if k != g:
- size_all_pairs[k,g] = len(U)*len(W)
-
-lamda_arr = np.ones((num_roles, num_roles,num_segments) , dtype=float)
-lamda_arr = 1e-1* np.random.randint(1,9, size=(num_roles, num_roles,num_segments))
-
-#set value for each iteration ( 0 - 8 )
-_itr = 8
-_itr = 0
-
-yu = (9-_itr)*.1
-lamda_arr[0,0]=[yu, 0.1]
-lamda_arr[0,1]= [0.1, yu]
-lamda_arr[1,0]=lamda_arr[0,1]
-lamda_arr[1,1]=[yu, yu]
-
-lamda_arr_act = np.zeros((num_roles, num_roles,num_segments) , dtype=float)
-
-change_points_arr = np.zeros((num_roles, num_roles, num_segments+1) , dtype=int)
-df_all= None
-
-points= list(range(0, (num_segments+1)*NO_SAMPLES, NO_SAMPLES))
-list1 = []
-
-# Generate piecewise non-homogeneous poisson process
-for k in range(0, num_roles):
- for g in range(k, num_roles):
- comb = []
- if k == g:
- comb = list(combinations(list_of_groups[k], 2))
- # print(type(comb))
- else:
- # comb = []
- key_data = [list_of_groups[k],list_of_groups[g],]
- comb = list(itertools.product(*key_data))
- # print(comb)
- if len(comb) != size_all_pairs[k,g]:
- print('not equal..')
-
- change_points_arr[k,g,:] = points
- lamda_arr[k,g,:] = lamda_arr[g,k,:]
-
- tot_count = np.zeros((num_segments) , dtype=float)
-
- for pair in comb:
-
- for d in range(0,num_segments):
-
- s = np.random.poisson(lamda_arr[k,g,d], NO_SAMPLES)
- # print(np.count_nonzero(s))
- tot_count[d] += np.count_nonzero(s)
-
- list1=[i for i, e in enumerate(s) if e != 0]
-
- if len(list1) == 0:
- print('zero')
-
- list1 = [x+points[d] for x in list1]
-
- df = pd.DataFrame(data=list1)
- df.columns =['timestamp']
-
- N= df.size
-
- list_start_stations =[pair[0]] * N
- list_end_stations =[pair[1]] * N
-
- df['source'] = list_start_stations
- df['target'] = list_end_stations
-
- df_all=pd.concat([df_all, df], ignore_index=True)
-
- for d in range(0,num_segments):
- lamda_arr_act[k,g,d] = tot_count[d]/(NO_SAMPLES*len(comb))
- # print(tot_count[d])
-## Other preparations
-
-# Remove self loops
-df_all = df_all[((df_all['source'] ) != (df_all['target']))]
-#sort
-df_all=df_all.sort_values('timestamp')
-df_all = df_all[['target', 'timestamp','source']]
-
-# Save as .csv file
-# df_all.to_csv('./Data/synthetic_ground_truth_g1.csv')
-
-df=df_all
-dest_folder='./Results/synthetic/3'
-t_df = df['timestamp']
-
-nodes_arr = np.union1d(df['target'],df['source']).astype(int)
-# list of nodes
-nodes = nodes_arr.tolist()
-num_vertices = len(nodes)
-
-# node-group dictionary
-group_dic = {}
-keys = nodes
-values = list(group_assignment)
-group_dic = dict(zip(keys,values))
-
-
-# create a new dictionary - key: node-pair , value: list of timestamps
-dic=df.groupby(['source','target'])['timestamp'].apply(list).to_dict()
-print('{} {} {} '.format(group_dic, lamda_arr_act,change_points_arr))
-
-
-liklihood_sum = sbm_core.compute_cost(group_dic,lamda_arr_act,change_points_arr,num_roles,num_segments,dic)
-print(' Initial Actual likelihood .......%f'%liklihood_sum)
-
-def _swap (row):
- if row['source'] > row['target']:
- row['source'] , row['target'] =row['target'] , row['source']
- return row
-
-# Undirected graph
-df=df.apply(lambda row: _swap(row), axis=1)
-#scale timestamps for zeroth reference point
-refValue = df['timestamp'].min()
-df['timestamp'] -= refValue
-
-# Experiment
-
-import experiment
-
-
-# User parameters
-num_roles=2
-num_segments=2
-num_levels=2# Optional arg
-algo_ver=3
-dest_folder='./Results/synthetic/'
-
-# tuning parameters
-theta = 0
-eta = 1
-tuning_params= {'theta':theta,'eta':eta}
-
-import time
-start_time = time.time()
-
-exp_obj = experiment.Experiment(df,num_roles,num_segments,algo_ver,dest_folder,tuning_params,num_levels,refValue)
-[itr_d,likelihood_d,group_dic_d,lambda_estimates_d,change_points_arr_d] = exp_obj.execute()
-
-print("--- %s seconds ---" % (time.time() - start_time))
-
-
-t_df = sorted(t_df)
-
-chg_points = change_points_arr_d[0,0,:]
-ranges_arr = [ [chg_points[s]+1,chg_points[s+1]] for s in range(0,len(chg_points)-1)]
-ranges_arr[0][0] = 0
-list_time_stamps = list(t_df)
-
-
-# iterate over timestamps list
-dis_arr = list()
-gt_arr = list()
-
-
-for item in list_time_stamps:
-
- # find the segment which the timestamp belongs
- # (is dependent on which groups the two nodes belong)
- d = sbm_core._findSegment(ranges_arr, len(ranges_arr) , int(item))
- dis_arr.append(d)
-
-
-chg_points = change_points_arr[0,0,:]
-ranges_arr = [ [chg_points[s]+1,chg_points[s+1]] for s in range(0,len(chg_points)-1)]
-ranges_arr[0][0] = 0
-list_time_stamps = list(t_df)
-
-
-# iterate over timestamps list
-
-for item in list_time_stamps:
-
- # find the segment which the timestamp belongs
- # (is dependent on which groups the two nodes belong)
- d = sbm_core._findSegment(ranges_arr, len(ranges_arr) , int(item))
- gt_arr.append(d)
-
-
-ind = adjusted_rand_score(gt_arr,dis_arr)
-print('rand index: seg {} : {}'.format(_itr, ind))
-
-g1= group_dic_d
-g2= group_dic_d[1]
-
-ds= list(group_dic_d.values() )
-gt1 = list(g1.values())
-
-ind_grp=adjusted_rand_score(ds,gt1)
-print('rand index: group {} : {}'.format(_itr, ind_grp))
-
-# likelihood for single group and single segment # Normlaized likelihood
-# num_roles=1
-# num_segments=1
-# num_levels=1
-# exp_obj = experiment.Experiment(df,num_roles,num_segments,algo_ver,dest_folder,tuning_params,num_levels,refValue)
-# exp_obj.execute()
-
-
-# 0.9757628970136779
-# 0.9766007299178403
-# 0.976928378298833
-# 0.9768191326709813
-# 0.9786828903097778
-# 0.9762078453564311
-# 0.9731767168042805
-# 0.9753286095154328
-# 0.5773249110735138
-
-
-# 3
-# 3
-# 2
-# 3
-# 3
-# 3
-# 3
-# 4
-# 4
-
-
-
diff --git a/lam-aff-1-ld.py b/lam-aff-1-ld.py
deleted file mode 100644
index 52c3e70..0000000
--- a/lam-aff-1-ld.py
+++ /dev/null
@@ -1,386 +0,0 @@
-
-
-"""
-Effect of lambda: LD
-Dataset-1
-"""
-import numpy as np
-import pandas as pd
-import utils
-import sbm_core
-import math
-from itertools import combinations
-import itertools
-from sklearn.metrics.cluster import adjusted_rand_score
-# Initilaize
-np.random.seed(155)
-
-res = np.zeros((9,5) , dtype=float)
-
-
-for _itr in range(8,9):
-
- num_roles=2
- num_vertices=20
- num_segments = 4
- num_levels = 2
-
- NO_SAMPLES= 200
- nodes = np.arange(num_vertices)
- lamda_arr_act = np.zeros((num_roles, num_roles,num_levels) , dtype=float)
-
-
- H =num_levels
- print('k-h levels %d'%(num_levels))
-
- # h-level lambda estimates
- lambda_estimates_h = np.random.rand(num_roles, num_roles, H)
- lambda_estimates_h = 1e-2*np.random.randint(11,99, size=(num_roles, num_roles, H))
-
- # Make high variant lambdas
-
- yu = (9-_itr)*.1
- lambda_estimates_h[0,0,:] = [yu, 0.1]
- lambda_estimates_h[0,1,:] = [0.1, yu]
- lambda_estimates_h[1,0,:] = lambda_estimates_h[0,1,:]
- lambda_estimates_h[1,1,:] = [yu, yu]
-
-
- l1 =list(range(0, H))
- l2 = []
- if num_segments > num_levels:
- l2 = [np.random.randint(0,H) for i in range(num_segments-H)]
-
- # Mapping from segment to a level
- g_mapping= np.array(l1 + l2)
- print('g mapping {}'.format(g_mapping))
-
- # initilaize group assignment randomly
- group_assignment_arr= np.random.randint(num_roles, size=(num_levels,num_vertices))
- # node-group dictionary
- group_dic = {}
-
- for i in range(0,num_levels ):
- level = i
-
- group_dic_level = {}
- keys = nodes
- values = list(group_assignment_arr[level])
- group_dic_level = dict(zip(keys,values))
- group_dic[i] = group_dic_level
- print('initial')
- # print(group_dic)
-
- for e_h in range(0,num_segments):
- g_a = group_dic[g_mapping[e_h]]
- list_of_groups= [[] for _ in range(num_roles)]
- for idx, val in g_a.items():
- list_of_groups[val].append(idx)
- print('group assignments {}: {}'.format(e_h,list_of_groups))
- # Plotting
-
- #Initialize lamda
- lamda_arr = np.zeros((num_roles, num_roles,num_segments) , dtype=float)
- for d in range(0, num_segments):
- for k in range(0, num_roles):
- for g in range(k, num_roles):
- lamda_arr[k,g, d]= lambda_estimates_h[k,g,g_mapping[d]]
- lamda_arr[g,k, d]= lamda_arr[k,g, d]
- change_points_arr = np.zeros((num_roles, num_roles, num_segments+1) , dtype=int)
- df_all= None
-
- points= list(range(0, (num_segments+1)*NO_SAMPLES, NO_SAMPLES))
- list1 = []
-
- level_seg_mapping = {}
- for d in range(num_segments):
- level = g_mapping[d]
- if level in level_seg_mapping:
- level_seg_mapping[level].append(d)
- else:
- level_seg_mapping[level] = []
- level_seg_mapping[level].append(d)
- # %%
- # Generate piecewise non-homogeneous poisson process
-
-
- tot_count = np.zeros((num_levels) , dtype=float)
- com_len = np.zeros((num_levels) , dtype=float)
- # for pair in comb:
-
- for i in range(0,num_levels):
- # i = g_mapping[d]
- group_assignment = group_assignment_arr[i]
-
- print(group_assignment)
-
- list_of_groups= [[] for _ in range(num_roles)]
-
- for idx, val in enumerate(group_assignment):
- list_of_groups[val].append(nodes[idx])
-
- # print(list_of_groups)
-
- size_all_pairs = {}
-
- for kk in range(0, num_roles):
- for gg in range(kk, num_roles):
- U=list_of_groups[kk]
- W=list_of_groups[gg]
-
- if kk == gg:
- size_all_pairs[kk,gg] = math.comb(len(U), 2)
- if kk != gg:
- size_all_pairs[kk,gg] = len(U)*len(W)
-
- for k in range(0, num_roles):
- for g in range(k, num_roles):
-
-
- change_points_arr[k,g,:] = points
- lamda_arr[k,g,:] = lamda_arr[g,k,:]
-
-
-
- comb = []
- if k == g:
- comb = list(combinations(list_of_groups[k], 2))
- # print(type(comb))
- else:
- # comb = []
- key_data = [list_of_groups[k],list_of_groups[g],]
- comb = list(itertools.product(*key_data))
- # print(comb)
- if len(comb) != size_all_pairs[k,g]:
- print('not equal..')
-
-
- print('d val {}'.format( d))
- com_len[i] = len(comb)
- # print('comb len {}'.format( com_len[d]))
- tot_count[i] = 0
-
- for pair in comb:
- s = np.random.poisson(lamda_arr[k,g,d], NO_SAMPLES)
- # print(np.count_nonzero(s))
- tot_count[i] += np.count_nonzero(s)
-
- list_org=[i for i, e in enumerate(s) if e != 0]
-
- if len(list_org) == 0:
- print('zero')
-
- for d in level_seg_mapping[i]:
-
-
- list1 = [x+points[d] for x in list_org]
- df= None
- df = pd.DataFrame(data=list1)
- df.columns =['timestamp']
-
- # print(list1)
- # if max(list1) > 799:
- # print('{} {}'.format(d, max(list1)))
- N= df.size
-
- # print(pair)
- # print(pair[0])
-
- list_start_stations =[pair[0]] * N
- list_end_stations =[pair[1]] * N
-
- df['source'] = list_start_stations
- df['target'] = list_end_stations
-
- df_all=pd.concat([df_all, df], ignore_index=True)
-
- # for dd in level_seg_mapping:
- # dd = d
- lamda_arr_act[k,g,i] = round(((tot_count[i])/(NO_SAMPLES*com_len[i])),1)
- lamda_arr_act[g,k,i] = lamda_arr_act[k,g,i]
- # print('tot count')
- # print(tot_count[dd])
- # print(' {} {} {} {} : k g d :lamb'.format(k,g,d,lamda_arr_act[g,k,dd]))
- print(' {} {} {} {} : k g d :lamb'.format(k,g,i,lamda_arr_act[g,k,i]))
-
- # Remove self loops
- df_all = df_all[((df_all['source'] ) != (df_all['target']))]
- #sort
- df_all=df_all.sort_values('timestamp')
- df_all = df_all[['target', 'timestamp','source']]
-
- # Save as .csv file
- # df_all.to_csv('./Data/synthetic_ground_truth_g1.csv')
-
-
- df= None
- df=df_all
- dest_folder='./Results/synthetic/3'
- t_df = df['timestamp']
-
- nodes_arr = np.union1d(df['target'],df['source']).astype(int)
- # list of nodes
- nodes = nodes_arr.tolist()
- num_vertices = len(nodes)
-
-
- # create a new dictionary - key: node-pair , value: list of timestamps
- dic=df.groupby(['source','target'])['timestamp'].apply(list).to_dict()
- print('{} {} {} '.format(group_dic, lamda_arr_act,change_points_arr))
-
-
- # liklihood_sum = sbm_core.mm_compute_cost(group_dic,lamda_arr_act,change_points_arr,num_roles,num_segments,dic,g_mapping)
- # print(' Initial Actual likelihood .......%f'%liklihood_sum)
-
- def _swap (row):
- if row['source'] > row['target']:
- row['source'] , row['target'] =row['target'] , row['source']
- return row
-
- # Undirected graph
- df=df.apply(lambda row: _swap(row), axis=1)
- #scale timestamps for zeroth reference point
- refValue = df['timestamp'].min()
- df['timestamp'] -= refValue
-
-
-
- chg_points = change_points_arr[0,0,:]
- ranges_arr = [ [chg_points[s]+1,chg_points[s+1]] for s in range(0,len(chg_points)-1)]
- ranges_arr[0][0] = 0
- list_time_stamps = list(t_df)
-
-
- # iterate over timestamps list
- gt_arr = list()
- for item in list_time_stamps:
-
- # find the segment which the timestamp belongs
- # (is dependent on which groups the two nodes belong)
- d = sbm_core._findSegment(ranges_arr, len(ranges_arr) , int(item))
- gt_arr.append(d)
-
- # Experiment
- import experiment
-
- # User parameters
- # num_roles=2
- # num_segments=10
- # num_levels=5# Optional arg
- algo_ver=4
- dest_folder='./Results/synthetic/'
-
- # tuning parameters
- theta = 1e-7
- eta = 1
- tuning_params= {'theta':theta,'eta':eta}
-
-
-
- exp_obj = experiment.Experiment(df,num_roles,num_segments,algo_ver,dest_folder,tuning_params,num_levels,refValue)
- # [likelihood_f,group_dic_f] = exp_obj.execute()
- [it,ll1,group_dic_d,lambda_estimates,change_points_arr_d]= exp_obj.execute()
-
-
- # SEGMENTATION ACCURACY
-
- t_df = sorted(t_df)
-
- chg_points = change_points_arr_d[0,0,:]
- ranges_arr = [ [chg_points[s]+1,chg_points[s+1]] for s in range(0,len(chg_points)-1)]
- ranges_arr[0][0] = 0
- list_time_stamps = list(t_df)
-
-
- # iterate over timestamps list
- dis_arr = list()
- for item in list_time_stamps:
-
- # find the segment which the timestamp belongs
- # (is dependent on which groups the two nodes belong)
- d = sbm_core._findSegment(ranges_arr, len(ranges_arr) , int(item))
- dis_arr.append(d)
-
-
- gt_arr= np.array(gt_arr, dtype=np.float64)
- dis_arr= np.array(dis_arr, dtype=np.float64)
- ind_seg = adjusted_rand_score(gt_arr,dis_arr)
- print('ind {} : {}'.format(_itr, ind_seg))
-
-
- liklihood_sum = sbm_core.mm_compute_cost(group_dic,lamda_arr_act,change_points_arr,num_roles,num_segments,dic,g_mapping)
- print(' Initial Actual likelihood .......%f'%liklihood_sum)
-
- print('g mapping {}'.format(g_mapping))
-
- for e_h in range(0,num_segments):
- g_a = group_dic[g_mapping[e_h]]
- list_of_groups= [[] for _ in range(num_roles)]
- for idx, val in g_a.items():
- list_of_groups[val].append(idx)
- print('group assignments {}: {}'.format(e_h,list_of_groups))
-
- #group ass, of level 1
- list_of_groups_1= [[] for _ in range(num_roles)]
- #group ass, of level 2
- list_of_groups_2= [[] for _ in range(num_roles)]
- g1= group_dic_d[0]
- g2= group_dic_d[1]
-
-
- found_cont = 0
-
- for i_h in range(0,num_levels):
- # i_h level
- grp = group_dic_d[i_h]
-
- list_of_groups_d= [[] for _ in range(num_roles)]
-
- for idx, val in grp.items():
- list_of_groups_d[val].append(idx)
-
- ds= list(group_dic_d[i_h].values() )
- gt1 = list(g1.values())
- gt2 = list(g2.values())
-
-
- ind1=adjusted_rand_score(ds,gt1)
- ind2=adjusted_rand_score(ds,gt2)
-
-
- d_in = max(ind1,ind2)
- found_cont += d_in
-
- ind = found_cont/2
-
- res[_itr][1] = ind
- res[_itr][4] = ind_seg
-print('end')
-
-
-
-# 0.989349
-# 0.9899235585218414
-# 0.9887209171780673
-# 0.9900141929986654
-# 0.9900915114849232
-# 0.9895393785077311
-# 0.9890441642420313
-# 0.5056343918828786
-# 0.489279
-
-
-
-# 2
-# 3
-# 2
-# 3
-# 3
-# 3
-# 4
-# 3
-# 3
-
-
-
-
diff --git a/lam-aff-2-fixed.py b/lam-aff-2-fixed.py
deleted file mode 100644
index 0ec8864..0000000
--- a/lam-aff-2-fixed.py
+++ /dev/null
@@ -1,262 +0,0 @@
-
-
-"""
-Affect of Lambda
-Dataset-2
-"""
-
-import numpy as np
-import pandas as pd
-import utils
-import sbm_core
-import math
-from itertools import combinations
-import itertools
-from sklearn.metrics.cluster import adjusted_rand_score
-# Initilaize
-np.random.seed(1137)
-
-num_roles=2
-num_vertices=25
-num_segments = 2
-
-NO_SAMPLES= 100
-group_assignment= np.random.randint(num_roles, size=(num_vertices))
-
-nodes = np.arange(num_vertices)
-
-list_of_groups= [[] for _ in range(num_roles)]
-
-for idx, val in enumerate(group_assignment):
- list_of_groups[val].append(nodes[idx])
-
-print(list_of_groups)
-
-size_all_pairs = {}
-for k in range(0, num_roles):
- for g in range(k, num_roles):
- U=list_of_groups[k]
- W=list_of_groups[g]
-
- if k == g:
- size_all_pairs[k,g] = math.comb(len(U), 2)
- if k != g:
- size_all_pairs[k,g] = len(U)*len(W)
-
-lamda_arr = np.ones((num_roles, num_roles,num_segments) , dtype=float)
-lamda_arr = 1e-1* np.random.randint(1,9, size=(num_roles, num_roles,num_segments))
-
-#set value for each iteration ( 0 - 8 )
-_itr = 8
-_itr = 0
-
-yu = (9-_itr)*.1
-lamda_arr[0,0]=[yu, 0.1]
-lamda_arr[0,1]= [0.1, yu]
-lamda_arr[1,0]=lamda_arr[0,1]
-lamda_arr[1,1]=[yu, yu]
-
-lamda_arr_act = np.zeros((num_roles, num_roles,num_segments) , dtype=float)
-
-change_points_arr = np.zeros((num_roles, num_roles, num_segments+1) , dtype=int)
-df_all= None
-
-points= list(range(0, (num_segments+1)*NO_SAMPLES, NO_SAMPLES))
-list1 = []
-
-# Generate piecewise non-homogeneous poisson process
-for k in range(0, num_roles):
- for g in range(k, num_roles):
- comb = []
- if k == g:
- comb = list(combinations(list_of_groups[k], 2))
- # print(type(comb))
- else:
- # comb = []
- key_data = [list_of_groups[k],list_of_groups[g],]
- comb = list(itertools.product(*key_data))
- # print(comb)
- if len(comb) != size_all_pairs[k,g]:
- print('not equal..')
-
- change_points_arr[k,g,:] = points
- lamda_arr[k,g,:] = lamda_arr[g,k,:]
-
- tot_count = np.zeros((num_segments) , dtype=float)
-
- for pair in comb:
-
- for d in range(0,num_segments):
-
- s = np.random.poisson(lamda_arr[k,g,d], NO_SAMPLES)
- # print(np.count_nonzero(s))
- tot_count[d] += np.count_nonzero(s)
-
- list1=[i for i, e in enumerate(s) if e != 0]
-
- if len(list1) == 0:
- print('zero')
-
- list1 = [x+points[d] for x in list1]
-
- df = pd.DataFrame(data=list1)
- df.columns =['timestamp']
-
- N= df.size
-
- list_start_stations =[pair[0]] * N
- list_end_stations =[pair[1]] * N
-
- df['source'] = list_start_stations
- df['target'] = list_end_stations
-
- df_all=pd.concat([df_all, df], ignore_index=True)
-
- for d in range(0,num_segments):
- lamda_arr_act[k,g,d] = tot_count[d]/(NO_SAMPLES*len(comb))
- # print(tot_count[d])
-## Other preparations
-
-# Remove self loops
-df_all = df_all[((df_all['source'] ) != (df_all['target']))]
-#sort
-df_all=df_all.sort_values('timestamp')
-df_all = df_all[['target', 'timestamp','source']]
-
-# Save as .csv file
-# df_all.to_csv('./Data/synthetic_ground_truth_g1.csv')
-
-df=df_all
-dest_folder='./Results/synthetic/3'
-t_df = df['timestamp']
-
-nodes_arr = np.union1d(df['target'],df['source']).astype(int)
-# list of nodes
-nodes = nodes_arr.tolist()
-num_vertices = len(nodes)
-
-# node-group dictionary
-group_dic = {}
-keys = nodes
-values = list(group_assignment)
-group_dic = dict(zip(keys,values))
-
-
-# create a new dictionary - key: node-pair , value: list of timestamps
-dic=df.groupby(['source','target'])['timestamp'].apply(list).to_dict()
-print('{} {} {} '.format(group_dic, lamda_arr_act,change_points_arr))
-
-
-liklihood_sum = sbm_core.compute_cost(group_dic,lamda_arr_act,change_points_arr,num_roles,num_segments,dic)
-print(' Initial Actual likelihood .......%f'%liklihood_sum)
-
-def _swap (row):
- if row['source'] > row['target']:
- row['source'] , row['target'] =row['target'] , row['source']
- return row
-
-# Undirected graph
-df=df.apply(lambda row: _swap(row), axis=1)
-#scale timestamps for zeroth reference point
-refValue = df['timestamp'].min()
-df['timestamp'] -= refValue
-
-# Experiment
-
-import experiment
-
-
-# User parameters
-num_roles=2
-num_segments=2
-num_levels=2# Optional arg
-algo_ver=3
-dest_folder='./Results/synthetic/'
-
-# tuning parameters
-theta = 0
-eta = 1
-tuning_params= {'theta':theta,'eta':eta}
-
-import time
-start_time = time.time()
-
-exp_obj = experiment.Experiment(df,num_roles,num_segments,algo_ver,dest_folder,tuning_params,num_levels,refValue)
-[itr_d,likelihood_d,group_dic_d,lambda_estimates_d,change_points_arr_d] = exp_obj.execute()
-
-print("--- %s seconds ---" % (time.time() - start_time))
-
-
-t_df = sorted(t_df)
-
-chg_points = change_points_arr_d[0,0,:]
-ranges_arr = [ [chg_points[s]+1,chg_points[s+1]] for s in range(0,len(chg_points)-1)]
-ranges_arr[0][0] = 0
-list_time_stamps = list(t_df)
-
-
-# iterate over timestamps list
-dis_arr = list()
-gt_arr = list()
-
-
-for item in list_time_stamps:
-
- # find the segment which the timestamp belongs
- # (is dependent on which groups the two nodes belong)
- d = sbm_core._findSegment(ranges_arr, len(ranges_arr) , int(item))
- dis_arr.append(d)
-
-
-chg_points = change_points_arr[0,0,:]
-ranges_arr = [ [chg_points[s]+1,chg_points[s+1]] for s in range(0,len(chg_points)-1)]
-ranges_arr[0][0] = 0
-list_time_stamps = list(t_df)
-
-
-# iterate over timestamps list
-
-for item in list_time_stamps:
-
- # find the segment which the timestamp belongs
- # (is dependent on which groups the two nodes belong)
- d = sbm_core._findSegment(ranges_arr, len(ranges_arr) , int(item))
- gt_arr.append(d)
-
-
-ind = adjusted_rand_score(gt_arr,dis_arr)
-print('rand index: seg {} : {}'.format(_itr, ind))
-
-g1= group_dic_d
-g2= group_dic_d[1]
-
-ds= list(group_dic_d.values() )
-gt1 = list(g1.values())
-
-ind_grp=adjusted_rand_score(ds,gt1)
-print('rand index: group {} : {}'.format(_itr, ind_grp))
-
-# 0.9785444674036701
-# 0.9791525131372905
-# 0.981440657362889
-# 0.9780947193990287
-# 0.9785576050121263
-# 0.9768656988977588
-# 0.9794087578274921
-# 0.9785467310928326
-# 0.8326828222297133
-
-
-# 3
-# 3
-# 3
-# 3
-# 3
-# 3
-# 5
-# 5
-# 5
-
-
-
-
diff --git a/sbm_core.py b/sbm_core.py
old mode 100755
new mode 100644
index 2ec7199..2da8eaa
--- a/sbm_core.py
+++ b/sbm_core.py
@@ -1,3 +1,5 @@
+
+
# ###################################################################################
# ### Utility Functions for Maximum Likelihood Estimation (MLE) and Segmentation ###
# ### ( based on stochastic blockmodels ) ###
@@ -21,26 +23,27 @@ def _findSegment(a, n, K):
# Binary search
while (end <= end):
-
- # mid point
- mid = (start + end) >> 1
-
- # element found
- if (K >= a[mid][0] and K <= a[mid][1]):
- return mid
-
- # first half
- elif (K < a[mid][0]):
- end = mid - 1
-
- # second half
- else:
- start = mid + 1
-
- # Not found
- print('Not Found')
- return -1
-
+
+ if end >= start:
+ # mid point
+ mid = (start + end) //2
+ # element found
+ if (K >= a[mid][0] and K <= a[mid][1]):
+ return mid
+
+ # first half
+ elif (K > a[mid][1]):
+ start = mid + 1
+
+ # second half
+ elif (K < a[mid][0]):
+ end = mid - 1
+ else:
+ # print('K : {} MID: {} START:{} END: {} ARRA : {}, n: {}'.format(K,mid, start,end, a,n))
+ # Not found
+ print('Not Found')
+ return -1
+
# split an empty group if it does exist
def _split_the_empty_group(num_roles,list_of_groups,group_assignment):
--
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