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utils.py
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utils.py
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import os, time, multiprocessing
import gc
import random
import numpy as np
import scipy
import scipy.spatial
import torch
def div_list(ls, n):
ls_len = len(ls)
if n <= 0 or 0 == ls_len:
return []
if n > ls_len:
return []
elif n == ls_len:
return [[i] for i in ls]
else:
j = ls_len // n
k = ls_len % n
ls_return = []
for i in range(0, (n - 1) * j, j):
ls_return.append(ls[i:i + j])
ls_return.append(ls[(n - 1) * j:])
return ls_return
def cal_rank(task, distance, distanceT, top_k, args):
acc_l2r, acc_r2l = np.array([0.] * len(top_k)), np.array([0.] * len(top_k))
mean_l2r, mean_r2l, mrr_l2r, mrr_r2l = 0., 0., 0., 0.
for i in range(len(task)):
ref = task[i]
indices = distance[i, :].argsort()
rank = np.where(indices == ref)[0][0]
mean_l2r += (rank + 1)
mrr_l2r += 1.0 / (rank + 1)
for j in range(len(top_k)):
if rank < top_k[j]:
acc_l2r[j] += 1
for i in range(len(task)):
ref = task[i]
indices = distanceT[:, i].argsort()
rank = np.where(indices == ref)[0][0]
mean_r2l += (rank + 1)
mrr_r2l += 1.0 / (rank + 1)
for j in range(len(top_k)):
if rank < top_k[j]:
acc_r2l[j] += 1
del distance, distanceT
gc.collect()
return (acc_l2r, mean_l2r, mrr_l2r, acc_r2l, mean_r2l, mrr_r2l)
#
# def multi_cal_rank(task, distance, top_k, args):
# acc = np.array([0.] * len(top_k))
# mean = 0.
# if len(distance)==3:
# distance1= distance[0]
# distance2= distance[1]
# distance3= distance[2]
# for i in range(len(task)):
# ref = task[i]
# indices1 = distance1[i, :].argsort()
# rank1 = np.where(indices1 == ref)[0][0]+1
# indices2 = distance2[i, :].argsort()
# rank2 = np.where(indices2 == ref)[0][0]+1
# indices3 = distance3[i, :].argsort()
# rank3 = np.where(indices3 == ref)[0][0]+1
# mean = mean+ (rank1+rank2+rank3)/3
#
#
#
# # mrr_l2r += 1.0 / (rank + 1)
# for j in range(len(top_k)):
# if rank1 <= top_k[j] and rank2 <= top_k[j] and rank3 <= top_k[j]:
# acc[j] += 1
# # for i in range(len(task)):
# # ref = task[i]
# # indices = distanceT[:, i].argsort()
# # rank = np.where(indices == ref)[0][0]
# # mean_r2l += (rank + 1)
# # mrr_r2l += 1.0 / (rank + 1)
# # for j in range(len(top_k)):
# # if rank < top_k[j]:
# # acc_r2l[j] += 1
# del distance1, distance2,distance3
# gc.collect()
#
#
# if len(distance) == 2:
# distance1 = distance[0]
# distance2 = distance[1]
#
# for i in range(len(task)):
# ref = task[i]
# indices1 = distance1[i, :].argsort()
# rank1 = np.where(indices1 == ref)[0][0] + 1
# indices2 = distance2[i, :].argsort()
# rank2 = np.where(indices2 == ref)[0][0] + 1
# mean = mean + (rank1 + rank2) / 2
#
# # mrr_l2r += 1.0 / (rank + 1)
# for j in range(len(top_k)):
# if rank1 <= top_k[j] and rank2 <= top_k[j]:
# acc[j] += 1
# # for i in range(len(task)):
# # ref = task[i]
# # indices = distanceT[:, i].argsort()
# # rank = np.where(indices == ref)[0][0]
# # mean_r2l += (rank + 1)
# # mrr_r2l += 1.0 / (rank + 1)
# # for j in range(len(top_k)):
# # if rank < top_k[j]:
# # acc_r2l[j] += 1
# del distance1, distance2
# gc.collect()
# return (acc, mean)
#
def multi_cal_rank(task, distance, top_k, args):
# acc = np.array([0.] * (len(top_k),3))# 二维列表的创建:
if args.evaluate_many:
acc = np.array([0.] * (len(top_k)))
else:
if len(distance)==3:
acc = np.array([[0. for col in range(3)] for row in range(len(top_k))])
else:
acc = np.array([[0. for col in range(2)] for row in range(len(top_k))])
# acc = np.array([[0. for col in range(3)] for row in range(len(top_k))])# 创建一个n*m的二维矩阵a,每个初值都是0mean = 0.
mean = 0.
if len(distance)==3:
# acc = np.array([0.] * (len(top_k),3))# 二维列表的创建:
# acc = np.array([[0. for col in range(3)] for row in range(len(top_k))]) # 创建一个n*m的二维矩阵a,每个初值都是0mean = 0.
distance1 = distance[0]
distance2 = distance[1]
distance3 = distance[2]
best_index=[]
for i in range(len(task)):
ref = task[i]
indices1 = distance1[i, :].argsort()
rank1 = np.where(indices1 == ref)[0][0]+1
indices2 = distance2[i, :].argsort()
rank2 = np.where(indices2 == ref)[0][0]+1
indices3 = distance3[i, :].argsort()
rank3 = np.where(indices3 == ref)[0][0]+1
mean = mean+ (rank1+rank2+rank3)/3
if args.evaluate_many:
for j in range(len(top_k)):
if rank1 <= top_k[j] and rank2 <= top_k[j] and rank3 <= top_k[j]:
acc[j] += 1
best_index.append(ref)
else:
for j in range(len(top_k)):
if rank1 <=top_k[j]:
acc[j][0] += 1
if rank2 <=top_k[j]:
acc[j][1] += 1
if rank3 <=top_k[j]:
acc[j][2] += 1
# mrr_l2r += 1.0 / (rank + 1)
# for j in range(len(top_k)):
# if rank1 <= top_k[j] and rank2 <= top_k[j] and rank3 <= top_k[j]:
# acc[j] += 1
# for i in range(len(task)):
# ref = task[i]
# indices = distanceT[:, i].argsort()
# rank = np.where(indices == ref)[0][0]
# mean_r2l += (rank + 1)
# mrr_r2l += 1.0 / (rank + 1)
# for j in range(len(top_k)):
# if rank < top_k[j]:
# acc_r2l[j] += 1
del distance1, distance2,distance3
gc.collect()
if len(distance) == 2:
# acc = np.array([0.] * (len(top_k),3))# 二维列表的创建:
# acc = np.array([[0. for col in range(2)] for row in range(len(top_k))]) # 创建一个n*m的二维矩阵a,每个初值都是0mean = 0.
distance1 = distance[0]
distance2 = distance[1]
best_index=[]
for i in range(len(task)):
ref = task[i]
indices1 = distance1[i, :].argsort()
rank1 = np.where(indices1 == ref)[0][0] + 1
indices2 = distance2[i, :].argsort()
rank2 = np.where(indices2 == ref)[0][0] + 1
mean = mean + (rank1 + rank2) / 2
if args.evaluate_many:
for j in range(len(top_k)):
if rank1 <= top_k[j] and rank2 <= top_k[j]:
acc[j] += 1
best_index.append(ref)
else:
for j in range(len(top_k)):
if rank1 <= top_k[j]:
acc[j][0] += 1
if rank2 <= top_k[j]:
acc[j][1] += 1
del distance1, distance2
gc.collect()
return (acc, mean,best_index)
def multi_cal_neg(pos, task, triples, r_hs_dict, r_ts_dict, ids, k):
neg = []
for _ in range(k):
neg_part = []
for idx, tas in enumerate(task):
(h, r, t) = pos[tas]
temp_scope, num = True, 0
while True:
h2, r2, t2 = h, r, t
choice = np.random.binomial(1, 0.5)
if choice:
if temp_scope:
h2 = random.sample(r_hs_dict[r], 1)[0]
else:
for id in ids:
if h2 in id:
h2 = random.sample(id, 1)[0]
# break
else:
if temp_scope:
t2 = random.sample(r_ts_dict[r], 1)[0]
else:
for id in ids:
if t2 in id:
t2 = random.sample(id, 1)[0]
# break
if (h2, r2, t2) not in triples:
break
else:
num += 1
if num > 10:
temp_scope = False
neg_part.append((h2, r2, t2))
neg.append(neg_part)
return neg
def multi_typed_sampling(pos, triples, ills, ids, k, params, thread=10):
t_ = time.time()
if len(pos[0]) == 2: # triple: 1:k
raise NotImplementedError("typed_sampling is not supported in ills sampling")
triples = set(triples)
r_hs_dict, r_ts_dict = {}, {}
for (h, r, t) in triples:
if r not in r_hs_dict:
r_hs_dict[r] = set()
if r not in r_ts_dict:
r_ts_dict[r] = set()
r_hs_dict[r].add(h)
r_ts_dict[r].add(t)
tasks = div_list(np.array(range(len(pos)), dtype=np.int32), thread)
pool = multiprocessing.Pool(processes=len(tasks))
reses = list()
for task in tasks:
reses.append(pool.apply_async(multi_cal_neg, (pos, task, triples, r_hs_dict, r_ts_dict, ids, k)))
pool.close()
pool.join()
neg_part = [[] for _ in range(k)]
for res in reses:
item = res.get() # (k, n, 3)
for i in range(k):
neg_part[i].extend(item[i])
neg = []
for part in neg_part:
neg.extend(part)
# print("\tmulti_typed_sampling time cost: {:.3f} s".format(time.time() - t_))
return neg
def typed_sampling(pos, triples, ills, ids, k, params):
t_ = time.time()
if len(pos[0]) == 2: # triple: 1:k
raise NotImplementedError("typed_sampling is not supported in ills sampling")
triples = set(triples)
r_hs_dict, r_ts_dict = {}, {}
for (h, r, t) in triples:
if r not in r_hs_dict:
r_hs_dict[r] = set()
if r not in r_ts_dict:
r_ts_dict[r] = set()
r_hs_dict[r].add(h)
r_ts_dict[r].add(t)
tasks = div_list(np.array(range(len(pos)), dtype=np.int32), 1)
neg_part = multi_cal_neg(pos, tasks[0], triples, r_hs_dict, r_ts_dict, ids, k)
neg = []
for part in neg_part:
neg.extend(part)
# print("\ttyped_sampling time cost: {:.3f} s".format(time.time() - t_))
return neg
def nearest_neighbor_sampling(pos, triples, ills, ids, k, params):
t_ = time.time()
emb = params["emb"]
metric = params["metric"]
if len(pos[0]) == 3: # triple: 1:k
sorted_id = [sorted(ids[0]), sorted(ids[1])]
distance = [- sim(emb[sorted_id[0]], emb[sorted_id[0]], metric=metric, normalize=False, csls_k=0), - sim(emb[sorted_id[1]], emb[sorted_id[1]], metric=metric, normalize=False, csls_k=0)]
cache_dict = {}
neg = []
triples = set(triples)
for _ in range(k):
for (h, r, t) in pos:
base_h = 0 if h in ids[0] else 1
base_t = 0 if t in ids[0] else 1
while True:
h2, r2, t2 = h, r, t
choice = np.random.binomial(1, 0.5)
if choice:
if h not in cache_dict:
indices = np.argsort(distance[base_h][sorted_id[base_h].index(h), :]) # descending=False
cache_dict[h] = np.array(sorted_id[base_h])[indices[1 : ]].tolist()
h2 = random.sample(cache_dict[h][ : k], 1)[0]
else:
if t not in cache_dict:
indices = np.argsort(distance[base_t][sorted_id[base_t].index(t), :]) # descending=False
cache_dict[t] = np.array(sorted_id[base_t])[indices[1 : ]].tolist()
t2 = random.sample(cache_dict[t][ : k], 1)[0]
if (h2, r2, t2) not in triples:
break
neg.append((h2, r2, t2))
elif len(pos[0]) == 2: # ill: 1:2k
neg_left = []
distance = - sim(emb[pos[:, 0]], emb[pos[:, 0]], metric=metric, normalize=False, csls_k=0)
for idx in range(len(pos)):
indices = np.argsort(distance[idx, :]) # descending=False
neg_left.append(pos[:, 0][indices[1 : k+1]])
neg_left = np.stack(neg_left, axis=1).reshape(-1, 1)
neg_right = []
distance = - sim(emb[pos[:, 1]], emb[pos[:, 1]], metric=metric, normalize=False, csls_k=0)
for idx in range(len(pos)):
indices = np.argsort(distance[idx, :]) # descending=False
neg_right.append(pos[:, 1][indices[1 : k+1]])
neg_right = np.stack(neg_right, axis=1).reshape(-1, 1)
neg_left = np.concatenate((neg_left, np.tile(pos, (k, 1))[:, 1].reshape(-1, 1)), axis=1).tolist()
neg_right = np.concatenate((np.tile(pos, (k, 1))[:, 0].reshape(-1, 1), neg_right), axis=1).tolist()
neg = neg_left + neg_right
del distance
gc.collect()
else:
raise NotImplementedError
# print("\tnearest_neighbor_sampling time cost: {:.3f} s".format(time.time() - t_))
return neg
def random_sampling(pos, triples, ills, ids, k, params):
t_ = time.time()
if len(pos[0]) == 3: # triple: 1:k
neg = []
triples = set(triples)
for _ in range(k):
for (h, r, t) in pos:
ent_set = ids[0] if h in ids[0] else ids[1]
while True:
h2, r2, t2 = h, r, t
choice = np.random.binomial(1, 0.5)
if choice:
h2 = random.sample(ent_set, 1)[0]
else:
t2 = random.sample(ent_set, 1)[0]
if (h2, r2, t2) not in triples:
break
neg.append((h2, r2, t2))
# if len(pos[0]) == 3: # triple: 1:k
# neg = []
# triples = set(triples)
# for _ in range(k):
# for (h, r, t) in pos:
#
# while True:
# h2, r2, t2 = h, r, t
# choice = np.random.binomial(1, 0.5)
# if choice:
# h2 = random.sample(ids[0], 1)[0]
# r2=random.sample(ids[1], 1)[0]
# else:
# t2 = random.sample(ids[2], 1)[0]
# r2=random.sample(ids[1], 1)[0]
# if (h2, r2, t2) not in triples:
# break
# neg.append((h2, r2, t2))
# elif len(pos[0]) == 4: # triple: 1:k
# neg = []
# for _ in range(k):
# for (e1, e2, e3, e4) in pos:
#
# h1, h2, h3, h4 = e1, e2, e3, e4
# choice = random.sample(range(0,4), 1)
# if choice==0:
# h1 = random.sample(ids[0] - {e1}, 1)[0]
# elif choice==1:
# h2 = random.sample(ids[1] - {e2}, 1)[0]
# elif choice == 2:
# h3 = random.sample(ids[2] - {e3}, 1)[0]
# elif choice == 3:
# h4 = random.sample(ids[3] - {e4}, 1)[0]
# neg.append((h1,h2,h3,h4))
# #固定e2
# e11 = random.sample(ids[0] - {e1}, 1)[0]
# e33 = random.sample(ids[2] - {e3}, 1)[0]
# e44 = random.sample(ids[3] - {e4}, 1)[0]
# neg_2.append((e11,e2,e33,e44))
# #固定e3
# e22 = random.sample(ids[1] - {e2}, 1)[0]
# e11 = random.sample(ids[0] - {e1}, 1)[0]
# e44 = random.sample(ids[3] - {e4}, 1)[0]
# neg_3.append((e11,e22,e3,e44))
# #固定e4
# e22 = random.sample(ids[1] - {e2}, 1)[0]
# e33 = random.sample(ids[2] - {e3}, 1)[0]
# e11 = random.sample(ids[0] - {e1}, 1)[0]
# neg_4.append((e11,e22,e33,e4))
# neg = neg_1 + neg_2+neg_3 + neg_4
elif len(pos[0]) == 2: # ill: 1:2k
neg_left, neg_right = [], []
ills = set([(e1, e2) for (e1, e2) in ills])
for _ in range(k):
for (e1, e2) in pos:
e11 = random.sample(ids[0] - {e1}, 1)[0]
neg_left.append((e11, e2))
e22 = random.sample(ids[1] - {e2}, 1)[0]
neg_right.append((e1, e22))
neg = neg_left + neg_right
# elif len(pos[0]) == 3:
# neg_1, neg_2, neg_3,= [], [], []
# ills = set([(e1, e2, e3) for (e1, e2, e3) in ills])
# for _ in range(k):
# for (e1, e2, e3) in pos:
# # 固定e1
# e22 = random.sample(ids[1] - {e2}, 1)[0]
# e33 = random.sample(ids[2] - {e3}, 1)[0]
# neg_1.append((e1, e22, e33))
# # 固定e2
# e11 = random.sample(ids[0] - {e1}, 1)[0]
# e33 = random.sample(ids[2] - {e3}, 1)[0]
# neg_2.append((e11, e2, e33))
# # 固定e3
# e22 = random.sample(ids[1] - {e2}, 1)[0]
# e11 = random.sample(ids[0] - {e1}, 1)[0]
# neg_3.append((e11, e22, e3))
#
#
# neg = neg_1 + neg_2 + neg_3
elif len(pos[0]) == 4:
neg_1, neg_2,neg_3,neg_4 = [], [],[], []
ills = set([(e1, e2, e3, e4) for (e1, e2, e3, e4) in ills])
for _ in range(k):
for (e1, e2, e3, e4) in pos:
#固定e1
e22 = random.sample(ids[1] - {e2}, 1)[0]
e33 = random.sample(ids[2] - {e3}, 1)[0]
e44 = random.sample(ids[3] - {e4}, 1)[0]
neg_1.append((e1,e22,e33,e44))
#固定e2
e11 = random.sample(ids[0] - {e1}, 1)[0]
e33 = random.sample(ids[2] - {e3}, 1)[0]
e44 = random.sample(ids[3] - {e4}, 1)[0]
neg_2.append((e11,e2,e33,e44))
#固定e3
e22 = random.sample(ids[1] - {e2}, 1)[0]
e11 = random.sample(ids[0] - {e1}, 1)[0]
e44 = random.sample(ids[3] - {e4}, 1)[0]
neg_3.append((e11,e22,e3,e44))
#固定e4
e22 = random.sample(ids[1] - {e2}, 1)[0]
e33 = random.sample(ids[2] - {e3}, 1)[0]
e11 = random.sample(ids[0] - {e1}, 1)[0]
neg_4.append((e11,e22,e33,e4))
neg = neg_1 + neg_2+neg_3 + neg_4
else:
raise NotImplementedError
# print("\trandom_sampling time cost: {:.3f} s".format(time.time() - t_))
return neg
# def SELF-DESIGN_sampling(pos, triples, ills, ids, k, params):
# '''SELF-DESIGN: sampling method implement'''
# This bootstrapping doesn't work well now, please use bootstrapping in semi_utils.py
def sim_boot(ref_sim_mat, id_1_set, id_2_set, cur, th, top=5):
t_ = time.time()
id_1_sort = np.array(sorted(id_1_set))
id_2_sort = np.array(sorted(id_2_set))
A, B = set(), set()
for (e1, e2) in cur:
A.add(e1)
B.add(e2)
mapping_dict = {}
for i in range(len(id_1_set)):
if id_1_sort[i] not in A:
vec = ref_sim_mat[i, :]
arg_res = np.argpartition(-vec, top)[:top]
mapping_dict[id_1_sort[i]] = [id_2_sort[arg_res], vec[arg_res]]
for i in range(len(id_2_set)):
if id_2_sort[i] not in B:
vec = ref_sim_mat[:, i]
arg_res = np.argpartition(-vec, top)[:top]
mapping_dict[id_2_sort[i]] = [id_1_sort[arg_res], vec[arg_res]]
new_ill = set()
for id in mapping_dict:
if id in id_1_set:
cor_id, cor_val = mapping_dict[id]
for i in range(top):
if cor_val[i] < th:
break
if cor_id[i] in mapping_dict and id in mapping_dict[cor_id[i]][0]:
new_ill.add((id, cor_id[i]))
break
labeled_alignment = None
A, B = [], []
if len(new_ill) > 0:
labeled_alignment = list(new_ill)
A = [a for (a, b) in labeled_alignment]
B = [b for (a, b) in labeled_alignment]
print("\tsim_boot time cost: {:.3f} s".format(time.time() - t_))
return labeled_alignment, A, B
from sklearn import preprocessing
from sklearn.metrics.pairwise import euclidean_distances
from scipy.spatial.distance import cdist
def sim(embed1, embed2, metric='inner', normalize=False, csls_k=0):
"""
Compute pairwise similarity between the two collections of embeddings.
Parameters
----------
embed1 : matrix_like
An embedding matrix of size n1*d, where n1 is the number of embeddings and d is the dimension.
embed2 : matrix_like
An embedding matrix of size n2*d, where n2 is the number of embeddings and d is the dimension.
metric : str, optional, inner default.
The distance metric to use. It can be 'cosine', 'euclidean', 'inner'.
normalize : bool, optional, default false.
Whether to normalize the input embeddings.
csls_k : int, optional, 0 by default.
K value for csls. If k > 0, enhance the similarity by csls.
Returns
-------
sim_mat : An similarity matrix of size n1*n2.
"""
if normalize:
embed1 = preprocessing.normalize(embed1)
embed2 = preprocessing.normalize(embed2)
if metric == 'inner':
sim_mat = np.matmul(embed1, embed2.T) # numpy.ndarray, float32
elif metric == 'cosine' and normalize:
sim_mat = np.matmul(embed1, embed2.T) # numpy.ndarray, float32
elif metric == 'euclidean':
# sim_mat = 1 - euclidean_distances(embed1, embed2)
sim_mat = (1 - euclidean_distances(embed1, embed2))/2+0.5
# print(type(sim_mat), sim_mat.dtype)
sim_mat = sim_mat.astype(np.float32)
elif metric == 'cosine':
sim_mat = 1 - cdist(embed1, embed2, metric='cosine') # numpy.ndarray, float64
# sim_mat = (1 - cdist(embed1, embed2, metric='cosine'))/2+0.5
sim_mat = sim_mat.astype(np.float32)
elif metric == 'manhattan':
sim_mat = 1 - cdist(embed1, embed2, metric='cityblock')
sim_mat = sim_mat.astype(np.float32)
else:
sim_mat = 1 - cdist(embed1, embed2, metric=metric)
sim_mat = sim_mat.astype(np.float32)
if csls_k > 0:
sim_mat = csls_sim(sim_mat, csls_k)
return sim_mat
def csls_sim(sim_mat, k):
"""
Compute pairwise csls similarity based on the input similarity matrix.
Parameters
----------
sim_mat : matrix-like
A pairwise similarity matrix.
k : int
The number of nearest neighbors.
Returns
-------
csls_sim_mat : A csls similarity matrix of n1*n2.
"""
nearest_values1 = calculate_nearest_k(sim_mat, k)
nearest_values2 = calculate_nearest_k(sim_mat.T, k)
csls_sim_mat = 2 * sim_mat - nearest_values1 - nearest_values2.T
return csls_sim_mat
def calculate_nearest_k(sim_mat, k):
sorted_mat = -np.partition(-sim_mat, k + 1, axis=1) # -np.sort(-sim_mat1)
nearest_k = sorted_mat[:, 0:k]
return np.mean(nearest_k, axis=1, keepdims=True)
# --- Code from AliNet(https://github.com/nju-websoft/AliNet) end ---
if __name__ == '__main__':
# TEST
emb = np.random.rand(20, 5)
ills = np.random.randint(1, 10, size=(9, 2))
triples = np.random.randint(1, 9, size=(5, 3)).tolist() + np.random.randint(11, 19, size=(5, 3)).tolist()
triples = [tuple(a) for a in triples]
print(emb)
print(ills)
print(triples)
print("emb.shape:", emb.shape)
print("ills.shape:", ills.shape)
print("triples.shape:", np.array(triples).shape)
k = 3
params = {
"emb": emb,
"metric": "euclidean"
}
ids = [set(range(1, 10)), set(range(11, 20))]
train = ills
print("ills as train:....")
# print(multi_typed_sampling(train, triples, ills, ids, k, params))
print("nearest_neighbor_sampling:")
smp = nearest_neighbor_sampling(train, triples, ills, ids, k, params)
print("shape:", np.array(smp).shape)
print(smp)
print("random_sampling:")
smp = random_sampling(train, triples, ills, ids, k, params)
print("shape:", np.array(smp).shape)
print(smp)
train = triples
print("triples as train:....")
print("multi_typed_sampling:")
smp = multi_typed_sampling(train, triples, ills, ids, k, params)
print("shape:", np.array(smp).shape)
print(smp)
print("nearest_neighbor_sampling:")
smp = nearest_neighbor_sampling(train, triples, ills, ids, k, params)
print("shape:", np.array(smp).shape)
print(smp)
print("random_sampling:")
smp = random_sampling(train, triples, ills, ids, k, params)
print("shape:", np.array(smp).shape)
print(smp)