def predict_rescal_als(T, idx): A, R, _, _, _ = rescal_als(T, 10, maxIter=10, lambda_A=10, lambda_R=10, compute_fit=False) n = A.shape[0] P = np.dot(A, np.dot(R[-1], A[idx, :].T)) nrm = np.linalg.norm(P) if nrm != 0: P = np.round_(P/nrm, decimals=3) return P
def tensor_factorization(lang, r, n_iter):
_log.info("start factorization")
X = pkl_utils._load(config.TENSOR[lang])
_log.info("data loading complete")
A, R, _, _, _ = rescal_als(X, r, maxIter=n_iter, lambda_A=10, lambda_R=10, compute_fit=False)
data_output = {'A':A, 'R':R}
pkl_utils._save(config.RESCAL_OUTPUT[lang], data_output)
_log.info("factorization complete")
def get_embeddings(Tenc):
feature_vec,R, fit, itr, exectimes = rescal_als(Tenc,250,
init='nvecs',
conv=1e-2,
lambda_A=0.1,
lambda_R= 0.1
)
return feature_vec
def predict_rescal_als(T):
A, R, _, _, _ = rescal_als(
T, 100, init='nvecs', conv=1e-3,
lambda_A=10, lambda_R=10
)
n = A.shape[0]
P = zeros((n, n, len(R)))
for k in range(len(R)):
P[:, :, k] = dot(A, dot(R[k], A.T))
return P
def predict_rescal_als(T):
A, R, f, itr, exectimes = rescal_als(T,
10,
init='nvecs',
lambda_A=10,
lambda_R=10)
n = A.shape[0]
P = zeros((n, n, len(R)))
for k in range(len(R)):
P[:, :, k] = dot(A, dot(R[k], A.T))
return A, P, R
def predict_rescal_als(T):
A, R, _, _, _ = rescal_als(T,
100,
init='nvecs',
conv=1e-3,
lambda_A=10,
lambda_R=10)
n = A.shape[0]
P = zeros((n, n, len(R)))
for k in range(len(R)):
P[:, :, k] = dot(A, dot(R[k], A.T))
return P
def predict_rescal_als(T, rank, lambda_A, lambda_R):
A, R, _, _, _ = rescal_als(T,
rank,
init='nvecs',
conv=1e-4,
lambda_A=lambda_A,
lambda_R=lambda_R,
compute_fit=True)
n = A.shape[0]
P = zeros((n, n, len(R)))
for k in range(len(R)):
P[:, :, k] = dot(A, dot(R[k], A.T))
return P
def predict_rescal_als(T, rank=100, lambda_A=10, lambda_R=10):
''' T is a list of sparse 2d lil_matrices.
'''
A, R, _, _, _ = rescal_als(T,
rank,
init='nvecs',
conv=1e-3,
lambda_A=lambda_A,
lambda_R=lambda_R)
n = A.shape[0]
P = np.zeros((n, n, len(R)))
for k in range(len(R)):
P[:, :, k] = np.dot(A, np.dot(R[k], A.T))
return P
def fit(self):
data = self.frontend.data_ma
K = self.expe.K
data = [sp.sparse.csr_matrix(data)]
A, R, fit, itr, exectimes = rescal_als(data,
K,
init='nvecs',
lambda_A=10,
lambda_R=10,
maxIter=self.iterations)
self.log.info('Rescal fit info : ')
print('fit: %s; itr: %s, exectimes: %s' % (fit, itr, exectimes))
self._theta = A
self._phi = R
def rescal(X, K):
## Load Matlab data and convert it to dense tensor format
#T = loadmat('data/alyawarra.mat')['Rs']
#X = [lil_matrix(T[:, :, k]) for k in range(T.shape[2])]
X = [sp.sparse.csr_matrix(X)]
A, R, fit, itr, exectimes = rescal_als(X,
K,
init='nvecs',
lambda_A=10,
lambda_R=10)
theta = A.dot(R).dot(A.T)
Y = 1 / (1 + np.exp(-theta))
Y = Y[:, 0, :]
Y[Y <= 0.5] = 0
Y[Y > 0.5] = 1
#Y = sp.stats.bernoulli.rvs(Y)
return Y
def fit(self, frontend):
self._init(frontend)
K = self.expe.K
y = frontend.adj()
data = [y]
A, R, fit, itr, exectimes = rescal_als(data,
K,
init='nvecs',
lambda_A=10,
lambda_R=10)
self._theta = A
self._phi = R[0]
self.log.info('rescal fit info: %s; itr: %s, exectimes: %s' %
(fit, itr, exectimes))
self.compute_measures()
if self.expe.get('_write'):
self.write_current_state(self)
def execute_rescal(input_tensor, rank, useNeedTypeSlice=True, useConnectionSlice=True, init='nvecs', conv=1e-4,
lambda_A=0, lambda_R=0, lambda_V=0):
temp_tensor = input_tensor.getSliceMatrixList()
if not (useNeedTypeSlice):
_log.info('Do not use needtype slice for RESCAL')
del temp_tensor[SparseTensor.NEED_TYPE_SLICE]
if not (useConnectionSlice):
_log.info('Do not use connection slice for RESCAL')
del temp_tensor[SparseTensor.CONNECTION_SLICE]
_log.info('start rescal processing ...')
_log.info('config: init=%s, conv=%f, lambda_A=%f, lambda_R=%f, lambda_V=%f' %
(init, conv, lambda_A, lambda_R, lambda_V))
_log.info('Datasize: %d x %d x %d | Rank: %d' % (
temp_tensor[0].shape + (len(temp_tensor),) + (rank,))
)
A, R, _, _, _ = rescal_als(
temp_tensor, rank, init=init, conv=conv,
lambda_A=lambda_A, lambda_R=lambda_R, lambda_V=lambda_V, compute_fit='true'
)
_log.info('rescal stopped processing')
return A, R
def netCreate(X, r, minEdges, sampleMethod='deterministic',
rescal_lambda_A=10, rescal_lambda_R=10,
plotting=True, layout='spring', graphScale=1.0):
"""Wrapper for sktensor.rescal_als. Create network by given
sampleMethod from hierarchical clustering of RESCALA_ALS
tensor factorization of singular values matrix A(A^T);
Input: X is list of sktensor.csr_matrices [X[k] for k in
relationships],
each X_k is frontal slide of adjacency tensor
(ie, adjacency matrix of one relationship type);
Return: {'cluster':hm, 'graph':g, 'linkage':hm['linkage'],
'theta':theta, 'A':A, 'Z':Z}
"""
#import logging
#from rescal import rescal_als
#import numpy as np
#import pandas as pd
#import networkx as nx
#import matplotlib.pyplot as plt
#from scipy.spatial.distance import pdist, squareform
# Set logging to INFO to see RESCAL information
logging.basicConfig(level=logging.INFO)
# Decompose tensor using RESCAL-ALS
A, R, fit, itr, exectimes = rescal_als(X, r, init='nvecs', lambda_A=rescal_lambda_A, lambda_R=rescal_lambda_R)
# make the AAT matrix
AAT = np.dot(A, A.T)
# heatmap
hm = heatmap(AAT)
plt.suptitle(r'A(A^T) HAC for Induced Rank = %s, $\lambda_{A}$ = %s, $\lambda_{R}$ = %s '%(r,rescal_lambda_A,rescal_lambda_R), fontweight='bold', fontsize=14)
# remove zeros
AATnn = AAT
AATnn[AATnn < 0] = 0
# remove upper triangle
AATnn = np.tril(AATnn, k= -1) # k = -1 to keep only below diagonal
# reproducibility
np.random.seed(1)
# network sampling
if sampleMethod == 'Bernoulli':
# shortcut: normalize by largest AAT (non-negative) value for separate bernoulli draws
# instead of summing over all AAT for multinomial draw, which is harder to
# transfer back and forth between vector and triangular matrix
theta = AATnn / AATnn.max()
# NETWORK SAMPLE ALGORITHM:
# random sample ties in network adjacency matrix
# one-element-at-a-time Bernoulli shortcut
# instead of multinomial sample of entire adjacency
n = np.shape(theta)[0]
m = np.shape(theta)[1]
Z = np.zeros((n,m))
# use dependent row,col permutations to randomly select
# elements ij to sample after first full pass through matrix
while np.sum(Z) < minEdges:
shuffledRows = np.arange(1,n) #up to n rows
np.random.shuffle(shuffledRows)
# first shuffle rows
for i in shuffledRows:
# for given row shuffle use lower triangle columns j in that row i
shuffledCols = np.arange(i) #up to (i-1) cols, ie, lower triangle
np.random.shuffle(shuffledCols)
for j in shuffledCols:
if Z[i,j] < 1:
Z[i,j] = np.random.binomial(n=1, p=theta[i,j], size=1)
if np.sum(Z) >= minEdges:
break
if np.sum(Z) >= minEdges:
break
elif sampleMethod == 'multinomial':
# NETWORK SAMPLING ALGORITHM:
# problem: doesn't sufficiently cluster the resulting network
draws = int(np.ceil(minEdges*1.2))
dist = pdist(A) # what matrix to use: pdist(A) or just tril(AAT) directly?
invdist = dist
invdist[invdist != 0] = 1/invdist[invdist!=0] # prevent division by 0
thetavec = invdist / np.sum(invdist)
theta = squareform(thetavec)
# multinomial sample
n = np.shape(theta)[0]
Z = np.zeros((n,n))
samp = sampleLinks(q=thetavec, edgesToDraw=1, draws=draws)
while np.sum(samp) < minEdges:
draws = int(np.ceil(draws * 1.1)) #increase number of draws and try again
samp = sampleLinks(q=thetavec,edgesToDraw=1,draws=draws)
Z[np.tril_indices_from(Z, k =-1)] = samp
elif sampleMethod == 'deterministic':
theta = AATnn / AATnn.max()
n = np.shape(AATnn)[0]
sv = AATnn[np.tril_indices_from(AATnn, k =-1)] #pull singular values from triangle
cutOff = topNEdges(data = sv, minEdges = minEdges,
n = n)['cutOff']
Z = np.zeros((n,n))
Z[np.where(AATnn >= cutOff)] = 1
else:
print('No valid sampleMethod selected. Please choose "Bernoulli", "multinomial", or "deterministic" .')
# NETWORK
# Create networkx graph from Z
g = nx.Graph()
#add nodes with colors of group
for n in np.arange(np.shape(hm['corder'])[0]-1):
g.add_node(hm['corder'][n],color=hm['group'][n])
nodeColorList = list(nx.get_node_attributes(g,'color').values())
#add edges with weight of theta (probability the link exists)
cardE = len(np.where(Z==1)[1])
edgeList = [(np.where(Z==1)[0][i], np.where(Z==1)[1][i]) for i in np.arange(cardE)]
edgeWeightList = theta[np.where(Z==1)] * (2 / max(theta[np.where(Z==1)])) #scaled link prob Pr(Z[i,j]=1) * weight
for e in np.arange(len(edgeList)-1):
g.add_edge(edgeList[e][0],edgeList[e][1],weight=edgeWeightList[e])
# NODE SIZES
# 1. cluster linkage importance
#nodesizelist = cluster['linkage'] * (400 / max(cluster['linkage']))
# 2. betweenness centrality (wide range of sizes; very small on periphery)
#nodesizelist = np.asarray(list(nx.betweenness_centrality(G,normalized=False).values())) * (400 / max(list(nx.betweenness_centrality(G,normalized=False).values())))
# 3. degree (smaller range of sizes; easier to see on the periphery)
nodeSizeList = np.asarray(list(g.degree().values())) * (350 / max(list(g.degree().values()))) #scaled so the largest is size 350
if plotting:
# reproducibility
np.random.seed(1)
#bc = nx.betweenness_centrality(g)
E = len(nx.edges(g))
V = len(g)
k = round(E/V,3)
#size = np.array(list(bc.values())) * 1000
# here replacing the hierarchical magnitude hm['corder']
fignx = plt.figure(figsize=(10,10))
## use heatmap color groupings to color nodes and heatmap magnitudes to size nodes
if layout == 'spring':
nx.draw(g, pos=nx.spring_layout(g, scale=graphScale),
node_color=nodeColorList, node_size=nodeSizeList,
width=edgeWeightList)
elif layout == 'fruchterman':
nx.draw(g, pos=nx.fruchterman_reingold_layout(g, scale=graphScale),
node_color=nodeColorList, node_size=nodeSizeList,
width=edgeWeightList)
else:
print('Please indicate at a valid layout.')
#else:
#nx.graphviz_layout(g, prog=graphProg)
plt.title('Network Created from Induced Rank = %s \n V = %s, E = %s, <k> = %s'%(r,V,E,k), fontweight='bold', fontsize=14)
#plot log degree sequence
degree_sequence=sorted(nx.degree(g).values(),reverse=True)
fig3 = plt.figure(figsize=(10,5))
plt.loglog(degree_sequence)
plt.title('Log Degree Distribution', fontweight='bold', fontsize=14)
return {'cluster':hm, 'graph':g, 'linkage':hm['linkage'], 'theta':theta, 'A':A, 'Z':Z}
import logging
from scipy.io.matlab import loadmat
from scipy.sparse import lil_matrix
from rescal import rescal_als
# Set logging to INFO to see RESCAL information
logging.basicConfig(level=logging.INFO)
# Load Matlab data and convert it to dense tensor format
T = loadmat('data/alyawarradata.mat')['Rs']
X = [lil_matrix(T[:, :, k]) for k in range(T.shape[2])]
# Decompose tensor using RESCAL-ALS
A, R, fit, itr, exectimes = rescal_als(X, 100, init='nvecs', lambda_A=10, lambda_R=10)
print 'Number of keys for T[1]:', len(t2_keys)
for i in range(10):
print ' ', t2_keys[i]
print 'Size of intersection:', len(set(t1_keys).intersection(set(t2_keys)))
print 'Running RESCAL code'
rank = 20
maxIter = 100
conv = 1e-5
lambda_A = 0
lambda_R = 0
A, R, _, _, _ = rescal_als(
[t[1].copy() for t in T],
rank,
init='nvecs',
conv=conv,
lambda_A=lambda_A,
lambda_R=lambda_A,
maxIter=maxIter,
)
print 'A shape:', A.shape
print 'R[0] shape:', R[0].shape
print 'R size:', len(R)
print '\nWriting A and R to disk'
a_matrix_file = out_dir + 'a_matrix.tsv'
r_matrix_file = out_dir + 'r_matrix.tsv'
out = open(a_matrix_file, 'w')
for n in range(node_dict.current_index - 1):
node = node_dict.getString(n+1)
def netCreate(X,
r,
minEdges,
sampleMethod='deterministic',
rescal_lambda_A=10,
rescal_lambda_R=10,
plotting=True,
layout='spring',
graphScale=1.0):
"""Wrapper for sktensor.rescal_als. Create network by given
sampleMethod from hierarchical clustering of RESCALA_ALS
tensor factorization of singular values matrix A(A^T);
Input: X is list of sktensor.csr_matrices [X[k] for k in
relationships],
each X_k is frontal slide of adjacency tensor
(ie, adjacency matrix of one relationship type);
Return: {'cluster':hm, 'graph':g, 'linkage':hm['linkage'],
'theta':theta, 'A':A, 'Z':Z}
"""
#import logging
#from rescal import rescal_als
#import numpy as np
#import pandas as pd
#import networkx as nx
#import matplotlib.pyplot as plt
#from scipy.spatial.distance import pdist, squareform
# Set logging to INFO to see RESCAL information
logging.basicConfig(level=logging.INFO)
# Decompose tensor using RESCAL-ALS
A, R, fit, itr, exectimes = rescal_als(X,
r,
init='nvecs',
lambda_A=rescal_lambda_A,
lambda_R=rescal_lambda_R)
# make the AAT matrix
AAT = np.dot(A, A.T)
# heatmap
hm = heatmap(AAT)
plt.suptitle(
r'A(A^T) HAC for Induced Rank = %s, $\lambda_{A}$ = %s, $\lambda_{R}$ = %s '
% (r, rescal_lambda_A, rescal_lambda_R),
fontweight='bold',
fontsize=14)
# remove zeros
AATnn = AAT
AATnn[AATnn < 0] = 0
# remove upper triangle
AATnn = np.tril(AATnn, k=-1) # k = -1 to keep only below diagonal
# reproducibility
np.random.seed(1)
# network sampling
if sampleMethod == 'Bernoulli':
# shortcut: normalize by largest AAT (non-negative) value for separate bernoulli draws
# instead of summing over all AAT for multinomial draw, which is harder to
# transfer back and forth between vector and triangular matrix
theta = AATnn / AATnn.max()
# NETWORK SAMPLE ALGORITHM:
# random sample ties in network adjacency matrix
# one-element-at-a-time Bernoulli shortcut
# instead of multinomial sample of entire adjacency
n = np.shape(theta)[0]
m = np.shape(theta)[1]
Z = np.zeros((n, m))
# use dependent row,col permutations to randomly select
# elements ij to sample after first full pass through matrix
while np.sum(Z) < minEdges:
shuffledRows = np.arange(1, n) #up to n rows
np.random.shuffle(shuffledRows)
# first shuffle rows
for i in shuffledRows:
# for given row shuffle use lower triangle columns j in that row i
shuffledCols = np.arange(
i) #up to (i-1) cols, ie, lower triangle
np.random.shuffle(shuffledCols)
for j in shuffledCols:
if Z[i, j] < 1:
Z[i, j] = np.random.binomial(n=1,
p=theta[i, j],
size=1)
if np.sum(Z) >= minEdges:
break
if np.sum(Z) >= minEdges:
break
elif sampleMethod == 'multinomial':
# NETWORK SAMPLING ALGORITHM:
# problem: doesn't sufficiently cluster the resulting network
draws = int(np.ceil(minEdges * 1.2))
dist = pdist(
A) # what matrix to use: pdist(A) or just tril(AAT) directly?
invdist = dist
invdist[
invdist != 0] = 1 / invdist[invdist != 0] # prevent division by 0
thetavec = invdist / np.sum(invdist)
theta = squareform(thetavec)
# multinomial sample
n = np.shape(theta)[0]
Z = np.zeros((n, n))
samp = sampleLinks(q=thetavec, edgesToDraw=1, draws=draws)
while np.sum(samp) < minEdges:
draws = int(np.ceil(draws *
1.1)) #increase number of draws and try again
samp = sampleLinks(q=thetavec, edgesToDraw=1, draws=draws)
Z[np.tril_indices_from(Z, k=-1)] = samp
elif sampleMethod == 'deterministic':
theta = AATnn / AATnn.max()
n = np.shape(AATnn)[0]
sv = AATnn[np.tril_indices_from(
AATnn, k=-1)] #pull singular values from triangle
cutOff = topNEdges(data=sv, minEdges=minEdges, n=n)['cutOff']
Z = np.zeros((n, n))
Z[np.where(AATnn >= cutOff)] = 1
else:
print(
'No valid sampleMethod selected. Please choose "Bernoulli", "multinomial", or "deterministic" .'
)
# NETWORK
# Create networkx graph from Z
g = nx.Graph()
#add nodes with colors of group
for n in np.arange(np.shape(hm['corder'])[0] - 1):
g.add_node(hm['corder'][n], color=hm['group'][n])
nodeColorList = list(nx.get_node_attributes(g, 'color').values())
#add edges with weight of theta (probability the link exists)
cardE = len(np.where(Z == 1)[1])
edgeList = [(np.where(Z == 1)[0][i], np.where(Z == 1)[1][i])
for i in np.arange(cardE)]
edgeWeightList = theta[np.where(Z == 1)] * (2 / max(
theta[np.where(Z == 1)])) #scaled link prob Pr(Z[i,j]=1) * weight
for e in np.arange(len(edgeList) - 1):
g.add_edge(edgeList[e][0], edgeList[e][1], weight=edgeWeightList[e])
# NODE SIZES
# 1. cluster linkage importance
#nodesizelist = cluster['linkage'] * (400 / max(cluster['linkage']))
# 2. betweenness centrality (wide range of sizes; very small on periphery)
#nodesizelist = np.asarray(list(nx.betweenness_centrality(G,normalized=False).values())) * (400 / max(list(nx.betweenness_centrality(G,normalized=False).values())))
# 3. degree (smaller range of sizes; easier to see on the periphery)
nodeSizeList = np.asarray(list(g.degree().values())) * (350 / max(
list(g.degree().values()))) #scaled so the largest is size 350
if plotting:
# reproducibility
np.random.seed(1)
#bc = nx.betweenness_centrality(g)
E = len(nx.edges(g))
V = len(g)
k = round(E / V, 3)
#size = np.array(list(bc.values())) * 1000
# here replacing the hierarchical magnitude hm['corder']
fignx = plt.figure(figsize=(10, 10))
## use heatmap color groupings to color nodes and heatmap magnitudes to size nodes
if layout == 'spring':
nx.draw(g,
pos=nx.spring_layout(g, scale=graphScale),
node_color=nodeColorList,
node_size=nodeSizeList,
width=edgeWeightList)
elif layout == 'fruchterman':
nx.draw(g,
pos=nx.fruchterman_reingold_layout(g, scale=graphScale),
node_color=nodeColorList,
node_size=nodeSizeList,
width=edgeWeightList)
else:
print('Please indicate at a valid layout.')
#else:
#nx.graphviz_layout(g, prog=graphProg)
plt.title(
'Network Created from Induced Rank = %s \n V = %s, E = %s, <k> = %s'
% (r, V, E, k),
fontweight='bold',
fontsize=14)
#plot log degree sequence
degree_sequence = sorted(nx.degree(g).values(), reverse=True)
fig3 = plt.figure(figsize=(10, 5))
plt.loglog(degree_sequence)
plt.title('Log Degree Distribution', fontweight='bold', fontsize=14)
return {
'cluster': hm,
'graph': g,
'linkage': hm['linkage'],
'theta': theta,
'A': A,
'Z': Z
}
if not os.path.exists(dest):
os.makedirs(dest, exist_ok=True)
for nt in range(n_test):
print(nt)
file_name = os.path.join(dest, 'init_%.3f_rescal_n_dim_%d_%d.txt' % (p_obs, n_dim, nt))
if not os.path.exists(file_name):
seq = list()
with open('../data/%s/train_%.3f.pkl' % (dataset, p_obs), 'rb') as f:
mask = pickle.load(f)
X = [csr_matrix(mask[k]) for k in range(n_relation)]
for i in range(budget):
try:
A, R, f, itr, exectimes = rescal.rescal_als(X, n_dim)
except:
A = np.random.random([n_entity, n_dim])
R = np.random.random([n_relation, n_dim, n_dim])
_X = np.zeros_like(T)
for k in range(T.shape[0]):
_X[k] = np.dot(np.dot(A, R[k]), A.T)
find = False
while not find:
_X[mask == 1] = -99999999
next_idx = np.unravel_index(_X.argmax(), _X.shape)
mask[next_idx] = 1
seq.append(next_idx)
if T[next_idx] == 1:
except ValueError:
print ' NO_PATH ',
print idx_to_name[e]
# This is the induced subgraph with the deleted edges.
# We can do RESCAL on the dependency matrix of this induced
# subgraph.
my_subgraph = g.induced_subgraph(cmp_22147)
my_subgraph_adj = lil_matrix((my_subgraph.vcount(),
my_subgraph.vcount()),
dtype='uint8')
for eel in my_subgraph.get_edgelist():
my_subgraph_adj[eel[0], eel[1]] = 1
T = [lil_matrix(my_subgraph_adj)]
A, R, _, _, _ = rescal_als(
T, 10, init='nvecs', conv=1e-3, lambda_A=1, lambda_R=1)
idx_of_org_in_cmp_22147 = cmp_22147.index(org)
als_prediction = np.dot(
np.dot(A[idx_of_org_in_cmp_22147], R[0]), A.T)
# Now based on this ranking find out what the rank of
# the true employees would be.
als_pred_of_employees_removed = [als_prediction[cmp_22147.index(e)]
for e in employees_to_remove]
sorted_als_pred = sorted(
filter(
lambda x: cmp_22147_mask[x[0]], enumerate(als_prediction)),
reverse=True,
key=lambda x: x[1])
sorted_als_pred_idx = [e[0] for e in sorted_als_pred]
sorted_als_pred_val = [e[1] for e in sorted_als_pred]
sparse = [lil_matrix((len(entities),len(entities))) for r in relations]
ind = 0
for rel in relations:
for s in rel:
for t in rel[s]:
sparse[ind][e2i[s],e2i[t]] = rel[s][t]
ind += 1
#plt.show()
fname = '{}{}.pkl'.format(sys.argv[1],sys.argv[2])
if os.path.isfile(fname) :
A,R,fit,itr,exectimes = pickle.load(open(fname,'r'))
else:
A, R, fit, itr, exectimes = rescal_als(sparse, int(sys.argv[2]), init='nvecs', conv=1e-6, lambda_A=1, lambda_R=1)
pickle.dump([A, R, fit, itr, exectimes],open(fname,'w'))
n = A.shape[0]
P = zeros((n, n, len(R)))
for k in range(len(R)):
P[:, :, k] = dot(A, dot(R[k], A.T))
print A.shape
print len(R),R[0].shape
plt.matshow(P[:,:,0])
#plt.show()
A_ = np.mean(A,axis=0)
e2vec = {}
for e in a2col:
e2vec[e] = A[e2i[e],:]
print e2vec
print 'Number of keys for T[1]:', len(t2_keys)
for i in range(10):
print ' ', t2_keys[i]
print 'Size of intersection:', len(set(t1_keys).intersection(set(t2_keys)))
print 'Running RESCAL code'
rank = 20
maxIter = 100
conv = 1e-5
lambda_A = 0
lambda_R = 0
A, R, _, _, _ = rescal_als(
[t[1].copy() for t in T],
rank,
init='nvecs',
conv=conv,
lambda_A=lambda_A,
lambda_R=lambda_A,
maxIter=maxIter,
)
print 'A shape:', A.shape
print 'R[0] shape:', R[0].shape
print 'R size:', len(R)
print '\nWriting A and R to disk'
a_matrix_file = out_dir + 'a_matrix.tsv'
r_matrix_file = out_dir + 'r_matrix.tsv'
out = open(a_matrix_file, 'w')
for n in range(node_dict.current_index - 1):
node = node_dict.getString(n + 1)
assert (len(entity) == 14951)
rel = data_table[1].unqiue()
assert (len(rel) == 1345)
entity_n = len(entity)
entity_index = {k: v for k, v in enumerate(entity)}
data_table.columns = ["head", "rel", "tail"]
# Packing it into a three-indices tensor.
print("Packaging Tensors...")
for r in rel:
tensor_slice = np.zeros([entity_n, entity_n])
rel_dataFrame = data_table[data_table["rel"].str.contains(r)]
for idx, row in rel_dataFrame.iterrows():
head_index = entity_index[row[0]]
tail_index = entity_index[row[2]]
tensor_slice[head_index, tail_index] = 1
tensor_slice = lil_matrix(tensor_slice)
rel_mat.append(tensor_slice)
return rel_mat
if __name__ == "__main__":
X = read_data("./FB15k/freebase_mtr100_mte100-train.txt")
print("Training starts.")
A, R, fit, itr, exectimes = rescal_als(X,
100,
init='nvecs',
lambda_A=10,
lambda_R=10)
def run_rescal(graph_dir, out_dir):
try_makedirs(out_dir)
T, node_dict, edge_dict = read_tensor_from_graph(graph_dir)
#T, node_dict, edge_dict = create_test_tensor()
print 'Samples from T[0] and T[1]:'
import random
t1_keys = T[0][1].keys()
random.shuffle(t1_keys)
print 'Relation for T[0]:', T[0][0]
print 'Number of keys for T[0]:', len(t1_keys)
for i in range(10):
print ' ', t1_keys[i]
t2_keys = T[1][1].keys()
random.shuffle(t2_keys)
print 'Relation for T[1]:', T[1][0]
print 'Number of keys for T[1]:', len(t2_keys)
for i in range(10):
print ' ', t2_keys[i]
print 'Size of intersection:', len(set(t1_keys).intersection(set(t2_keys)))
print 'Running RESCAL code'
rank = 20
maxIter = 100
conv = 1e-5
lambda_A = 0
lambda_R = 0
A, R, _, _, _ = rescal_als(
[t[1].copy() for t in T],
rank,
init='nvecs',
conv=conv,
lambda_A=lambda_A,
lambda_R=lambda_R,
maxIter=maxIter,
)
print 'A shape:', A.shape
print 'R[0] shape:', R[0].shape
print 'R size:', len(R)
print '\nWriting A and R to disk'
a_matrix_file = out_dir + 'a_matrix.tsv'
r_matrix_file = out_dir + 'r_matrix.tsv'
out = open(a_matrix_file, 'w')
for n in range(node_dict.current_index - 1):
node = node_dict.getString(n+1)
out.write(node)
for j in range(rank):
out.write('\t')
out.write(str(A[n,j]))
out.write('\n')
out.close()
out = open(r_matrix_file, 'w')
for index, r in enumerate(R):
relation = T[index][0]
out.write(relation)
out.write('\n')
for i in range(rank):
for j in range(rank):
out.write(str(r[i,j]))
if (j < rank - 1):
out.write('\t')
out.write('\n')
out.write('\n')
out.close()
return A, R, rank, edge_dict, node_dict