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run.py
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import collections
import math
import os
import random
import sys
from functools import partial
from multiprocessing import Pool
from os.path import join
import matplotlib.pyplot as plt
import numpy as np
import scipy.io
import scipy.sparse as sparse
from cne.cne_known import ConditionalNetworkEmbedding_K
from query import *
from utils import *
def test_for_s(folder_split, partial_net, test_e, y_true, X, post_P, ne_params, step_size, budget, strategy, ne_id, sid):
s = strategy[sid]
print('s', s, sid)
score_s = []
bgt = budget
cur_partial_net = partial_net.copy()
cur_X = X.copy()
cur_post_P = post_P.copy()
while bgt > 0:
print('bgt', bgt)
y_pred = predict(cur_post_P, test_e)
score_s.append(eval_prediction(y_true, y_pred, eval_t))
query = get_query(cur_partial_net, cur_post_P, cur_X, step_size, bgt, ne_params, s)
print(s, 'len(query)', len(query), query[-10:], score_s[-1])
cur_partial_net = update_partial_net(cur_partial_net, query, full_A)
cur_X, cur_post_P = embed(cur_partial_net, None, ne_params)
bgt -= len(query)
y_pred = predict(cur_post_P, test_e)
score_s.append(eval_prediction(y_true, y_pred, eval_t))
to_cache(folder_split+'/'+s+'_ne_'+str(ne_id)+'.pkl', score_s)
return score_s
def generate_pool(eids, size):
return np.array(random.sample(list(eids), size))
def one_split_all_s(p, folder_split, full_A, r_0, stp_s, case, bgt_k, pool_size, target_size, strategy, split_id, nr_ne):
n = full_A.shape[0]
all_eid = e_to_eid(n, from_csr_matrix_to_edgelist(sparse.triu(np.ones_like(full_A), 1)))
if case == 1:
# Case-1
S0_eid = memoize(generate_S0, folder_split+'/S0_'+str(split_id)+'.pkl', refresh=False)(full_A, r_0)
U_eid = np.array(list(set(all_eid) - set(S0_eid)))
print('S0_eid', len(S0_eid))
partial_net0 = get_partial_net(full_A, S0_eid, U_eid)
elif case == 2:
# Case-2
S0_eid = memoize(generate_S0, folder_split+'/S0_'+str(split_id)+'.pkl', refresh=False)(full_A, r_0)
U_eid = np.array(list(set(all_eid) - set(S0_eid)))
print('S0_eid', len(S0_eid))
pool_eid = memoize(generate_pool, folder_split+'/pool_'+str(split_id)+'.pkl', refresh=False)(U_eid, pool_size)
print('pool_eid size', len(pool_eid))
partial_net0 = get_partial_net(full_A, S0_eid, U_eid, pool_eid)
elif case == 3:
# Case-3
S0_eid, target_eid = memoize(split_node_pairs, folder_split+'/split_'+str(split_id)+'.pkl', refresh=False)(full_A, r_0, target_size)
U_eid = np.array(list(set(all_eid) - set(S0_eid)))
print('S0_eid', len(S0_eid))
print('target_eid', len(target_eid))
rest_u_eid = np.array(list(set(U_eid) - set(target_eid)))
pool_eid = memoize(generate_pool, folder_split+'/pool_'+str(split_id)+'.pkl', refresh=False)(rest_u_eid, pool_size)
print('pool_eid', len(pool_eid))
partial_net0 = get_partial_net(full_A, S0_eid, U_eid, pool_eid, target_eid)
else:
# You can also define your partial net, P, and T here.
print('No such Case.')
target_e = partial_net0['target_e']
y_true = full_A[target_e[:, 0], target_e[:, 1]]
size_unknown = len(partial_net0['u_eid'])
print('nr_unknown_e', size_unknown)
step_size = stp_s
budget = bgt_k
print('budget', budget, 'stp_s', stp_s)
for ne_id in range(nr_ne):
X0, post_P0 = memoize(embed, folder_split+'/NE_'+str(ne_id)+'.pkl', refresh=False)(partial_net0, None, ne_params)
res_test_id = p.map(partial(test_for_s, folder_split, partial_net0,
target_e, y_true, X0, post_P0, ne_params,
step_size, budget, strategy, ne_id),
list(range(len(strategy))))
return 0
if __name__ == "__main__":
# changing dataset
dataname = 'celegans'
# changing parameters for ALPINE experiment
Case = 2
r_0 = 0.03
nr_split = 2
nr_ne = 2
# parameters for embedding method
ne_params = {"name": "cne", "d": 8, "s1": 1, "s2": 32,
"optimizer": {"name": "adam", "lr": 0.1, "max_iter": 2000}}
# load data
if not os.path.exists(dataname):
os.makedirs(dataname)
full_A = load_data(dataname)
strategy, labels = strategy_collections()
n = full_A.shape[0]
m = 0.5*n*(n-1)
eval_t = 1
stp_s = int(m*0.01)
# config active learning setting
if Case == 1:
bgt_k = stp_s*10 # changing parameter
pool_size = None
target_size = None
folder = dataname+'/TU_PU_r0_'+str(int(r_0*100))+'_s'+str(ne_params['s2'])+'_split'+str(nr_split)+'_ne'+str(nr_ne)+'_stp'+str(stp_s)+'_bgt'+str(bgt_k)
elif Case == 2:
bgt_k = stp_s*5 # changing parameter
pool_size = stp_s*10 # changing parameter
target_size = None
folder = dataname+'/TU_r0_'+str(int(r_0*100))+'_s'+str(ne_params['s2'])+'_split'+str(nr_split)+'_ne'+str(nr_ne)+'_stp'+str(stp_s)+'_bgt'+str(bgt_k)+'_P'+str(pool_size)
elif Case == 3:
bgt_k = stp_s*5 # changing parameter
pool_size = stp_s*10 # changing parameter
target_size = stp_s*5 # changing parameter
folder = dataname+'/r0_'+str(int(r_0*100))+'_s'+str(ne_params['s2'])+'_split'+str(nr_split)+'_ne'+str(nr_ne)+'_stp'+str(stp_s)+'_bgt'+str(bgt_k)+'_P'+str(pool_size)+'_T'+str(target_size)
else:
print('No such Case.')
if not os.path.exists(folder):
os.makedirs(folder)
print(folder)
# run experiment
res = []
p = Pool(processes=len(strategy))
for split_id in range(nr_split):
folder_split = folder+'/split_'+str(split_id)
if not os.path.exists(folder_split):
os.makedirs(folder_split)
res_split_id = one_split_all_s(p, folder_split, full_A,
r_0, stp_s, Case, bgt_k,
pool_size, target_size,
strategy, split_id, nr_ne)
res.append(res_split_id)
print(res)
# print averaged the scores
avg_scores = {s: [] for s in labels}
print(avg_scores)
for split_id in range(nr_split):
for s_id in range(len(strategy)):
for ne_id in range(nr_ne):
path_id = folder+'/split_'+str(split_id)+'/'+strategy[s_id]+'_ne_'+str(ne_id)+'.pkl'
tmp = from_cache(path_id)
if s_id <= 2:
avg_scores[labels[0]].append(tmp)
else:
avg_scores[labels[s_id-2]].append(tmp)
res_avg_scores = {}
for j in range(len(labels)):
score_j = list(np.mean(np.array(avg_scores[labels[j]]), axis=0))
res_avg_scores[labels[j]] = score_j
print(res_avg_scores)
# plot the results
l_s = len(res_avg_scores[labels[0]])
print(l_s)
x = np.linspace(0, l_s, l_s)
for j in range(len(labels)):
score_j = res_avg_scores[labels[j]]
print(labels[j], score_j)
plt.plot(x, score_j, label=labels[j])
plt.legend()
plt.savefig(folder+'/results.png')
plt.close()