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MMSBM_calc.py
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# coding: utf-8
# In[106]:
import pandas as pd
import numpy as np
from string import ascii_lowercase
from copy import deepcopy
from numba import jit,prange,int64,double,vectorize,float64
from time import time
import os,sys
import time as time_lib
import argparse
import yaml
# In[32]:
#Parametros del sistema
super_tak = time()
parser = argparse.ArgumentParser()
parser.add_argument("-K",help="Number of node groups",type=int)
parser.add_argument("-L",help="Number of items groups",type=int)
parser.add_argument("--lambda_nodes",help="Intensity of nodes priors",type=float)
parser.add_argument("--lambda_items",help="Intensity of items priors",type=float)
parser.add_argument("-F","--Fold",help="Number of the fold",type=int)
parser.add_argument("-s","--seed",help="Optiona, seed to generate the random matrices",type=int)
parser.add_argument("-N","--N_itt",help="Maximum number of iterations",type=int)
parser.add_argument("-n","--N_meas",help="Number of iterationts to check the convergence",type=int)
parser.add_argument("-R","--Redo",help="Redo simulation if it was done before", action='store_true')
parser.add_argument("--N_simu",help="Optional, simulation number to label the simulation. It will substitute the seed to name the simulation.",type=int)
parser.add_argument("--dir_format",help="Directory format, addig information about the lambdes (lambdes) or the groups number (groups)",default="lambdes",choices=["lambdes","groups"],type=str)
args = parser.parse_args()
config_file_name = "config.yaml"
with open(config_file_name) as fp:
data = yaml.load(fp)
def choose_params(arg,value,arg_value):
if arg==None:
param = value[arg_value]
else: param = arg
return param
def use_default(default,non_default,arg):
try:
if non_default[arg]!=None: return non_default[arg]
else: return default
except:
return default
if args.K==None:
K = data['users']['K']
else: K = args.K
if args.L==None:
L = data['items']['L']
else: L = args.L
if args.lambda_nodes==None:
try:
lambda_nodes = data['users']['lambda_nodes']
except: lambda_nodes = 0.0
else: lambda_nodes = args.lambda_nodes
if args.lambda_items==None:
try:
lambda_items = data['items']['lambda_items']
except: lambda_items = 0.0
else: lambda_items = args.lambda_items
seed = args.seed
N_simu = args.N_simu
N_itt = choose_params(args.N_itt,data['simulation'],'N_itt')
print('N_itt',N_itt,args.N_itt)
N_measure = choose_params(args.N_meas,data['simulation'],'N_measure')
N_fold = choose_params(args.Fold,data,'N_fold')#data['N_fold']
if lambda_nodes==0.0:node_meta_data = []
else:node_meta_data = data['users']['nodes_meta']
if lambda_items==0.0:
items_meta_data = []
Taus = []
else:
items_meta_data = data['items']['items_meta']
Taus = data['items']['Taus']
N_meta_nodes = len(node_meta_data)
N_meta_items = len(items_meta_data)
print('file' not in data['items'])
if 'file' in data['users']:
node_file_dir = data['folder']+'/'+data['users']['file']
else:
node_file_dir = ''
if 'file' in data['items']:
item_file_dir = data['folder']+'/'+data['items']['file']
else:
item_file_dir = ''
links_base_file_dir = data['folder']+'/'+data['links']['training'].replace('{F}',str(N_fold))
links_test_file_dir = data['folder']+'/'+data['links']['test'].replace('{F}',str(N_fold))
node_header = data['users']['nodes_header']
item_header = data['items']['items_header']
rating_header = data['links']['rating_header']
node_separator = use_default('\t',data['users'],'separator')
item_separator = use_default('\t',data['items'],'separator')
link_separator_base = use_default('\t',data['links'],'separator_training')
link_separator_test = use_default('\t',data['links'],'separator_test')
print('separators',link_separator_base,link_separator_test,node_separator,item_separator)
# In[7]:
if args.dir_format == 'lambdes': simu_dir_lam = 'simu_ln_{}_li_{}'.format(lambda_nodes,lambda_items)
else: simu_dir_lam = 'simu_K_{}_L_{}'.format(K,L)
if not os.path.exists(simu_dir_lam):
try:
os.makedirs(simu_dir_lam)
except: pass
if N_simu==None:
direct = simu_dir_lam+'/results_simu_s_{}_f_{}'.format(seed,N_fold)
else:
direct = simu_dir_lam+'/results_simu_{}_f_{}'.format(N_simu,N_fold)
simu_dir = direct
print('l_nodes={}\nl_items={}\nK={}\nL={}\nfold={}\nseed={}'.format(lambda_nodes,lambda_items,K,L,N_fold,seed))
if args.Redo==False:
if os.path.exists(direct+'/total_p.dat'):
print('ja estava feta!!!')
exit()
else: print('nofeta!!!')
if not os.path.exists(simu_dir):
os.makedirs(simu_dir)
if not os.path.exists(direct):
os.makedirs(direct)
if sys.version_info[0] < 3:
df_links = pd.read_csv(links_base_file_dir.format(N_fold),sep=link_separator_base.encode('utf-8'), engine='python')
if 'file' in data['users']:df_nodes = pd.read_csv(node_file_dir,sep=node_separator.encode('utf-8'), engine='python')#queryodf(nodes_query, engine="IMPALA", use_cache=False, block=True)
else:df_nodes = pd.DataFrame()#
if 'file' in data['items']:df_items = pd.read_csv(item_file_dir,dtype={'node_id': np.int64, 'common':str},sep=item_separator.encode('utf-8'), engine='python')
else:df_items = pd.DataFrame()#queryodf(items_query, engine="IMPALA", use_cache=False, block=True)
links_test_df = pd.read_csv(links_test_file_dir.format(N_fold),sep=link_separator_test.encode('utf-8'), engine='python')
else:
df_links = pd.read_csv(links_base_file_dir.format(N_fold),sep=link_separator_base, engine='python')
if 'file' in data['users']:df_nodes = pd.read_csv(node_file_dir,sep=node_separator, engine='python')#queryodf(nodes_query, engine="IMPALA", use_cache=False, block=True)
else:df_nodes = pd.DataFrame()
print(df_nodes.head())
print('-----',node_separator)
if 'file' in data['items']:df_items = pd.read_csv(item_file_dir,sep=item_separator,dtype={'node_id': np.int64, 'genre_id':str}, engine='python')#queryodf(items_query, engine="IMPALA", use_cache=False, block=True)
else:df_items = pd.DataFrame()
links_test_df = pd.read_csv(links_test_file_dir.format(N_fold),sep=link_separator_test, engine='python')
print(links_test_df.head())
links_test = links_test_df[[node_header,item_header]].values
N_att_meta_items = []
for meta in items_meta_data:
df_items[meta] = df_items[meta].astype('str')
try:
df_items[meta] = df_items[meta].str.split('|')
except AttributeError:
for j,l in enumerate(df_items[meta]):
df_items[meta][j] = [df_items[meta][j]]
#df_items[meta+"_id"] = df_items[meta+"_id"].str.split('|')
N_att_meta_items.append(len(set(df_items[meta].values.sum())))
# In[10]:
if 'file' in data['users']: N_nodes = len(df_nodes)
else: N_nodes = max(df_links.max()[node_header],links_test_df.max()[node_header])+1
# In[11]:
N_links = len(df_links)
# In[12]:
if 'file' in data['items']: N_items = len(df_items)
else: N_items = max(df_links.max()[item_header],links_test_df.max()[item_header])+1
print(N_nodes,N_items)
# In[47]:
def obtain_links_arrays(node_header,item_header,lambda_nodes,lambda_items,rating_header):
if node_meta_data != None:
factor_meta_nodes = lambda_nodes*float(len(node_meta_data))
else:
factor_meta_nodes = 1.0e-16
if items_meta_data != None:
factor_meta_items = 1.0e-16
for i,meta in enumerate(items_meta_data):
factor_meta_items += lambda_items*float(N_att_meta_items[i])
else:
factor_meta_items = 1.0e-16
Links_observed = len(df_links)
veins_nodes={user:df[item_header].values for user,df in df_links.groupby(node_header)}
print('veins nodes calculats')
veins_items={item:df[node_header].values for item,df in df_links.groupby(item_header)}
print('veins items calculats')
links_by_ratings={rating:df[[node_header,item_header]].values for rating,df in df_links.groupby(rating_header)}
print('links per rating calculats')
links_array = df_links[[node_header,item_header]].values
links_ratings = df_links[rating_header].values
print('arrays calculats')
print('pasant a arrays:')
N_veins_nodes = []
N_veins_items = []
veins_items_array = []
veins_nodes_array = []
print('uep0',df_links.head())
for item in range(N_items):
if item in veins_items:
N_veins_items.append(float(len(veins_items[item]))+1e-16+factor_meta_items)
veins_items_array.append(veins_items[item])
else:
N_veins_items.append(factor_meta_items+1e-16)
veins_items_array.append([])
#El 2 es el número de metadatos exclusivos!! En el caso dado son edad y género. Si hago un binding de ambos es uno!!
for node in range(N_nodes):
if node in veins_nodes:
N_veins_nodes.append(float(len(veins_nodes[node]))+1e-16)
veins_nodes_array.append(veins_nodes[node])
else:
veins_nodes_array.append([])
N_veins_nodes.append(1e-16)
#PART METADADES
if node_meta_data==None: continue
for meta in node_meta_data:
#print(node,meta,df_nodes[df_nodes.nodeid==node],df_nodes[df_nodes.nodeid==node][meta].values)
if not pd.isnull(df_nodes[df_nodes[node_header]==node][meta].values[0]):
N_veins_nodes[-1] += lambda_nodes
#else:
# print('aqui!!',node)
print('ya esta')
#Pasem a arrays els enllaços
N_ratings = len(links_by_ratings)
links_by_ratings_array = [links_by_ratings[i] for i in range(N_ratings)]
print('ya esta tot!!!!')
return N_ratings,links_array,links_ratings,links_by_ratings_array,veins_nodes_array,veins_items_array,N_veins_nodes,N_veins_items
N_ratings,links_array,links_ratings,links_by_ratings_array,veins_nodes_array,veins_items_array,N_veins_nodes,N_veins_items = obtain_links_arrays(node_header,item_header,lambda_nodes,lambda_items,rating_header)
# In[105]:
#@vectorize([float64(float64, float64, float64)])
@jit
def any_nan(M):
return np.any(np.isnan(M))
def obtain_meta_arrays(meta_list,id_header,observed):
#Metadatos nodods
Att_meta = []
N_att_meta = []
metas_links_arrays = []
veins_metas = []
veins_nodes = []
N_veins_metas = []
print(meta_list,id_header,observed)
df_filtred = df_nodes[df_nodes[id_header].isin(observed)]
#print('aqui',len(df_filtred))
if meta_list==None:
return Att_meta,N_att_meta,metas_links_arrays,veins_metas,N_veins_metas
for meta in meta_list:
Att_meta.append(list(set(df_nodes[meta][df_nodes[meta].notnull()])))
N_att_meta.append(len(Att_meta[-1]))
#Arrays
metas_links_arrays.append(df_nodes[[id_header,meta]][df_nodes[meta].notnull()].astype(int).values)
#veins_metas.append([df_filtred[id_header][df_filtred[meta]==int(att)].values for att in range(N_att_meta[-1])])
veins_metas.append([df_nodes[id_header][df_nodes[meta]==int(att)].values for att in range(N_att_meta[-1])])
N_veins_metas.append([float(len(arr)) for arr in veins_metas[-1]])
veins_nodes.append(np.ones(len(df_nodes),dtype=np.int32))
for n,att in metas_links_arrays[-1]:
veins_nodes[-1][n] = att
return Att_meta,N_att_meta,metas_links_arrays,veins_metas,veins_nodes,N_veins_metas
observed_nodes = np.unique(links_array[:,0])
nodes_no_observed = np.array([i for i in range(N_nodes) if i not in observed_nodes])
print('uep1 ',df_nodes.head())
#print('aqui',len(observed),len(df_nodes))
Att_meta_nodes,N_att_meta_nodes,metas_links_arrays_nodes,veins_metas_nodes,veins_nodes_metas,N_veins_metas_nodes = obtain_meta_arrays(node_meta_data,node_header,observed_nodes)
veins_items_metas = []
veins_items_metas_ones = []
veins_metas_items = []
veins_metas_items_ones = []
metas_links_arrays_items = []
metas_links_arrays_items_type = []#Label de s'enllaç 0/1
N_veins_metas_items = []
observed_items = np.unique(links_array[:,1])
items_no_observed = np.array([i for i in range(N_items) if i not in observed_items])
print('uep 2')
#category_items_inverse = {category_items[cat]:cat for cat in category_items}
for meta in range(len(items_meta_data)):
veins_items_metas.append(np.ones((N_items,N_att_meta_items[meta]),dtype=np.int32))
veins_items_metas[-1] *= np.arange(0,N_att_meta_items[meta])[:,np.newaxis].T
veins_metas_items.append(np.ones((N_att_meta_items[meta],N_items),dtype=np.int32)*np.arange(0,N_items)[:,np.newaxis].T)
veins_metas_items_ones.append([])
veins_items_metas_ones.append([[] for j in range(N_items)])
metas_links_arrays_items.append(np.zeros((N_att_meta_items[meta]*N_items,2),dtype=np.int32))
N_veins_metas_items.append([N_items for i in range(N_att_meta_items[meta])])
metas_links_arrays_items_type.append([])
i = 0
for g in range(N_att_meta_items[meta]):
df_metas = df_items[df_items[items_meta_data[meta]].apply(lambda x: str(g) in x)][item_header]
veins_metas_items_ones[-1].append(df_metas.values)
#print(veins_metas_items_ones[-1])
for j in range(N_items):
metas_links_arrays_items[-1][i][0] = j
metas_links_arrays_items[-1][i][1] = g
if j in df_metas:
metas_links_arrays_items_type[-1].append(1)
else:
metas_links_arrays_items_type[-1].append(0)
if j in observed_items:
veins_items_metas_ones[-1][j].append(g)
i += 1
metas_links_arrays_items_type[-1] = np.array(metas_links_arrays_items_type[-1])
print('ya estan los metas!!')
# In[94]:
#print(veins_metas_items)
# In[142]:
#np.seterr(all='raise')
def timer(func):
def crono(*args):
tic = time()
to_return = func(*args)
tac = time()
print(func.__name__,'ha trigat en executarse',tac-tic)
return to_return
return crono
# In[143]:
def print_n_func(f):
def func(*args):
#f_jit = jit()(f)
try:
return f(*args)
except FloatingPointError:
print('error de FloatingPointError a',f.__name__,f.__name__=='p_kl_comp_arrays')
if f.__name__=='p_kl_comp_arrays':
for i,varName in zip(range(len(args)),['omega','p_kl','eta','theta','K','L']):
if type(args[i])==type(np.array([])):
print('before',varName,args[i])
args[i][args[i][:,:]<1e-16] =0
print('after',varName,args[i])
else:
for i in range(len(args)):
if type(args[i])==type(np.array([])):
if len(args[i].shape)==2:
args[i][args[i][:,:]<1e-16] =0
elif len(args[i].shape)==4:
args[i][args[i][:,:,:,:]<1e-16] =0
else:
args[i][args[i][:]<1e-16] =0
return f(*args)
except TypeError:
print('error de TypeError a',f.__name__)
return func
# In[144]:
@print_n_func
#@vectorize([float64(float64, float64, float64)])
def sum_matrix_lambda(m1,m2,l):
return m1+l*m2
# In[145]:
#@timer
@jit(cache=True,nopython=True)
def finished(theta,theta_old,N_elements,tol):
finished = False
if(np.sum(np.abs(theta-theta_old))/(N_elements)<tol):
finished = True
return finished
# In[147]:
@print_n_func
#@timer
@jit(locals=dict(i=int64,j=int64,k=int64,l=int64,suma=double,new_theta=double[:,:]),parallel=True)
def theta_comp_arrays(omega,theta,K,veins_nodes_array,N_veins_nodes):
new_theta = np.array(theta)
for i,veins in enumerate(veins_nodes_array):
for k in prange(K):
#theta_ik = theta[i,k]
new_theta[i,k] = np.sum(omega[i,veins,k,:])
new_theta[i,k] /= N_veins_nodes[i]
return new_theta
#@print_n_func
#@timer
@jit(cache=True,locals=dict(i=int64,j=int64,k=int64,l=int64,suma=double,new_theta=double[:,:]),parallel=True)
def theta_comp_arrays_multilayer_2(omega_metas,omega,theta,K,veins_nodes_array,N_veins_nodes,veins_metas_nodes,N_att_meta_nodes):
new_theta = np.zeros((N_nodes,K))
N_metas = len(N_att_meta_nodes)
if lambda_nodes==0:
means = []
for meta,N_att in enumerate(N_att_meta_nodes):
means.append(np.zeros((K,N_att)))
for att in range(N_att):
c = 0.0
for k in range(K):
means[-1][k,att] = np.sum(theta[veins_metas_nodes[meta][att],k])/len(veins_metas_nodes[meta][att])
c += means[-1][k,att]
means[-1][:,att] /= c
for i in prange(N_nodes):
veins_node = veins_nodes_array[i]
if veins_node==[]:
if lambda_nodes==0:
for meta in range(N_metas):
a = veins_nodes_metas[meta][i]
new_theta[i,:] = means[meta][:,a]
new_theta[i,:] = new_theta[i,:]/np.sum(new_theta[i,:])
#print('aillat',i,new_theta[i,:])
continue
for meta in range(N_metas):
new_theta[i,:] += omega_metas[meta][i,:]
new_theta[i,:] *= lambda_nodes/N_veins_nodes[i]
else:
for meta in range(N_metas):
new_theta[i,:] += lambda_nodes*omega_metas[meta][i,:]
for k in prange(K):
#theta_ik = theta[i,k]
new_theta[i,k] += np.sum(omega[i,veins_node,k,:])
new_theta[i,:] /= N_veins_nodes[i]
return new_theta
#@print_n_func
#@timer
@jit()
def theta_comp_arrays_multilayer(omega_metas,omega,theta,K,observed_nodes,nodes_no_observed,N_veins_nodes):
#new_theta = np.zeros((N_nodes,K))
N_metas = len(omega_metas)
means = np.sum(theta[observed_nodes,:],axis=0)/float(len(observed_nodes))
#means /= means#.sum()
new_theta = omega.sum(axis=1).sum(axis=2)
for meta in range(N_metas):
new_theta += omega_nodes[meta].sum(axis=1)*lambda_nodes
new_theta /= N_veins_nodes
if lambda_nodes == 0 and nodes_no_observed!=[]:new_theta[nodes_no_observed] = means
return new_theta
#@print_n_func
#@timer
#@jit(locals=dict(i=int64,j=int64,k=int64,l=int64,suma=double,new_theta=double[:,:]),parallel=True)
def theta_comp_arrays_exclusive(omega,theta,K,links_array,veins_nodes_array,N_veins_metas_nodes,veins_metas_nodes,veins_nodes_metas,N_att,N_veins_nodes):
new_theta = np.zeros((N_nodes,K))
if lambda_nodes==0:
means = np.zeros((K,N_att))
for att in range(N_att):
c = 0.0
for k in range(K):
means[k,att] = np.sum(theta[veins_metas_nodes[att],k])/len(veins_metas_nodes[att])
c += means[k,att]
means[:,att] /= c
for link in prange(len(links_array)):
i = links_array[link][0]
a = links_array[link][1]
if lambda_nodes==0 and veins_nodes_array[i]==[]:
new_theta[i,:] = means[:,a]
#print('aillat',i,new_theta[i,:])
continue
for k in prange(K):
new_theta[i,k] = omega[i,k]
new_theta[i,:] /= N_veins_nodes[i]
#if any_nan(new_theta[i,:]):
# print('no aillat',i,omega[i,k])
#if any_nan(new_theta):exit()
return new_theta
#@print_n_func
#@timer
#@jit(cache=True,parallel=True)
def eta_multilayer(eta,omega,omega_flat,N_veins_items,lambda_items,L,Taus,items_no_observed,N_att_meta_items):
new_eta = np.zeros((N_items,L))
N_metas = len(N_att_meta_items)
means = np.sum(eta[observed_items,:],axis=0)/len(observed_items)
new_eta += omega.sum(axis=0).sum(axis=1)
start = 0
for meta,n in enumerate(N_att_meta_items):
omega_meta = omega_flat[start:start+n*L*N_items*Taus[meta]].reshape(N_items,n,L,Taus[meta])
new_eta += omega_meta.sum(axis=1).sum(axis=2)*lambda_items
start += n*L*N_items*Taus[meta]
for l in range(L):
new_eta[:,l] /= N_veins_items
if lambda_items==0 and items_no_observed!=[]:
new_eta[items_no_observed] = means
return new_eta
# In[6]:
#@print_n_func
#@timer
@jit(cache=True,parallel=True)
def eta_multilayer_2(eta,omega,omega_items,veins_items_array,L,N_veins_items,lambda_items,veins_metas_items,veins_items_metas,veins_items_metas_ones,N_att_meta_items):
new_eta = np.zeros((N_items,L))
N_metas = len(N_att_meta_items)
means = []
if lambda_items==0:
for meta,N_att in enumerate(N_att_meta_items):
means.append(np.zeros((L,N_att)))
for att in range(N_att):
c = 0.0
for l in range(L):
means[-1][l,att] = np.sum(eta[veins_metas_items[meta][att],l])/len(veins_metas_items[meta][att])
c += means[-1][l,att]
means[-1][:,att] /= c
for j in prange(N_items):
veins = veins_items_array[j]
if veins==[]:
if lambda_items==0:
for meta in range(N_metas):
a = veins_items_metas[meta][j]
new_eta[j,:] = means[meta][:,a]
new_eta[j,:] = new_eta[j,:]/np.sum(new_eta[j,:])
#print('aillat',i,new_theta[i,:])
continue
#for k in prange(K):
for meta,omega_meta in enumerate(omega_items):
meta_veins = veins_items_metas[meta][j]
#print('------------>',j,omega_meta[j,meta_veins,l,:],np.sum(omega_meta[j,meta_veins,l,:]))
#raw_input()
new_eta[j,:] += np.sum(omega_meta[j,meta_veins,:,:],axis=(0,2))#*lambda_items
new_eta[j,:] *= lambda_items/N_veins_items[j]
#print('------------>2',new_eta[j,:],lambda_items,N_veins_items[j],lambda_items/N_veins_items[j])
else:
for l in prange(L):
for meta,omega_meta in enumerate(omega_items):
meta_veins = veins_items_metas[meta][j]
new_eta[j,l] += np.sum(omega_meta[j,meta_veins,l,:])*lambda_items
#print(j,np.sum(omega_meta[j,meta_veins,l,:])*lambda_items/N_veins_items[j])
#raw_input()
new_eta[j,l] += np.sum(omega[veins,j,:,l])
new_eta[j,:] /= N_veins_items[j]
#print(j,new_eta[j,:])
#raw_input()
return new_eta
# In[6]:
@print_n_func
#@timer
@jit(cache=True,locals=dict(i=int64,j=int64,k=int64,l=int64,suma=double,new_eta=double[:,:]))
def eta_comp_arrays(omega,eta,L,veins_items_array,N_veins_items):
new_eta = np.array(eta)
for j,veins in enumerate(veins_items_array):
for l in range(L):
#eta_jl = eta[j,l]
new_eta[j,l] = np.sum(omega[veins,j,:,l])
new_eta[j,l] /= N_veins_items[j]
return new_eta
# In[150]:
# In[7]:
@print_n_func
#@timer
@jit(cache=True,nopython=True,parallel=True)
def p_kl_comp_arrays(omega,p_kl,eta,theta,K,L,links_array,links_ratings):
p_kl[:,:,:] = 0
for k in range(K):
for l in prange(L):
for link in prange(len(links_ratings)):
i = links_array[link][0]
j = links_array[link][1]
rating = links_ratings[link]
p_kl[k,l,rating] += omega[i,j,k,l]
suma = np.sum(p_kl[k,l,:])
p_kl[k,l,:] /= (suma+1e-16)
return p_kl
# In[151]:
# In[20]:
@print_n_func
#@timer
@jit(cache=True,nopython=True)
def q_ka_comp_arrays(omega,q_ka,K,links_array,att_elements):
q_ka2 = np.zeros((K,len(att_elements)))
for link in range(len(links_array)):
i = links_array[link][0]
a = links_array[link][1]
for k in range(K):
#print(i,k,a)
q_ka2[k,a] = omega[i,a,k]#/att_elements[a]
suma = np.expand_dims(np.sum(q_ka2,axis =1),axis=1)
q_ka2 /=suma
return q_ka2
# In[152]:
# In[9]:
#@print_n_func
#@timer
#@jit(cache=True,nopython=True,locals=dict(i=int64,j=int64,k=int64,l=int64,suma=double),parallel=True)
@jit(nopython=True,locals=dict(i=int64,j=int64,k=int64,l=int64,suma=double))
def omega_comp_arrays(omega,p_kl,eta,theta,K,L,links_array,links_ratings):
#new_omega = np.array(omega)
for link in range(len(links_ratings)):
i = links_array[link][0]
j = links_array[link][1]
rating = links_ratings[link]
omega[i,j,:,:] = p_kl[:,:,rating]*(np.expand_dims(theta[i,:], axis=1)@np.expand_dims(eta[j,:],axis=0))
suma = omega[i,j,:,:].sum()
omega[i,j,:,:] /= suma+1e-16
return omega
# In[117]:
# In[10]:
@print_n_func
#@timer
#@jit(nopython=True,locals=dict(i=int64,a=int64,k=int64,link=int64,suma=double),parallel=True)
@jit(nopython=True,locals=dict(i=int64,a=int64,k=int64,link=int64,suma=double))
def omega_comp_arrays_exclusive(omega,q_ka,theta,N_nodes,N_att_meta):
o = np.zeros(omega.shape)
for i in range(N_nodes):
for a in range(int(N_att_meta)):
o[i,a,:] = theta[i,:]*q_ka[:,a]
s = o.sum(axis=2)+1e-16
o /= np.expand_dims(s, axis=2)
return o
@jit(nopython=True,locals=dict(i=int64,a=int64,k=int64,link=int64,suma=double),parallel=True)
def omega_comp_arrays_exclusive(omega,q_ka,theta,N_nodes,metas_links_arrays_nodes):
for j in prange(len(metas_links_arrays_nodes)):
i = metas_links_arrays_nodes[j,0]
a = metas_links_arrays_nodes[j,1]
s = 0
for k in range(K):
omega[i,a,k] = theta[i,k]*q_ka[k,a]
s +=omega[i,a,k]
omega[i,a,:] /= s
return omega
@print_n_func
#@timer
@jit(nopython=True,parallel=True)
def total_p_comp_test(N_nodes,N_items,N_ratings,K,L,theta,eta,p_kl,test):
total_p = np.zeros((len(test),N_ratings))
for n in prange(len(test)):
i = test[n,0]
j = test[n,1]
for r in range(N_ratings):
suma = 0
for k in range(K):
for l in range(L):
suma += theta[i,k]*eta[j,l]*p_kl[k,l,r]
total_p[n,r] += suma
return total_p
# In[85]:
# return Att_meta_nodes,N_att_meta_nodes,metas_links_arrays,veins_metas,N_veins_metas
def inicialitzacio(K,L,Taus,N_nodes,N_items,N_ratings,N_att_meta_nodes,N_att_meta_items,links_array,links_ratings,metas_links_arrays_nodes,metas_links_arrays_items):
#Inicialitzacio
##Definim matrius
theta = np.random.rand(N_nodes,K)
eta = np.random.rand(N_items,L)
p_kl = np.random.rand(K,L,N_ratings)
suma = np.sum(theta,axis =1)
theta /= suma[:,np.newaxis]
suma = np.sum(eta,axis=1)
eta /= suma[:,np.newaxis]
suma = np.sum(p_kl,axis =2)
p_kl /=suma[:,:,np.newaxis]
omega = np.zeros((N_nodes,N_items,K,L),dtype=np.double)
omega = omega_comp_arrays(omega,p_kl,eta,theta,K,L,links_array,links_ratings)
q_l_taus = []
zetes = []
omega_items = []
for i in range(len(items_meta_data)):
Tau = Taus[i]
zetes.append(np.random.rand(N_att_meta_items[i],Tau))
suma = np.sum(zetes[-1],axis=1)
zetes[-1] /= suma[:,np.newaxis]
q_l_taus.append(np.random.rand(L,Tau,N_att_meta_items[i]))
suma = np.sum(q_l_taus[-1],axis =2)
q_l_taus[-1] /= suma[:,:,np.newaxis]
omega_items.append(np.zeros((N_items,N_att_meta_items[i],L,Tau),dtype=np.double))
omega_items[-1] = omega_comp_arrays(omega_items[-1],q_l_taus[-1],zetes[-1],eta,L,Tau,metas_links_arrays_items[i],metas_links_arrays_items_type[i])
q_kas = []
omega_nodes = []
for meta,N in enumerate(N_att_meta_nodes):
q_kas.append(np.random.rand(K,N))
##Normalitzem
suma = np.sum(q_kas[-1],axis =1)
q_kas[-1] /=suma[:,np.newaxis]
omega_nodes.append(np.zeros((N_nodes,N_att_meta_nodes[-1],K),dtype=np.double))
omega_nodes[-1] = omega_comp_arrays_exclusive(omega_nodes[-1],q_kas[-1],theta,N_nodes,metas_links_arrays_nodes[-1])
#omega_comp_arrays.inspect_types()
'''simu_dir = "input_matrix"
np.savetxt(simu_dir+'/theta.dat'.format(N_run),theta)
np.savetxt(simu_dir+'/eta.dat'.format(N_run),eta)
#np.savetxt(simu_dir+'/eta_{}.dat'.format(N_run),eta)
for r in range(N_ratings):
np.savetxt(simu_dir+'/pkl_{}.dat'.format(r),p_kl[:,:,r])
for meta in range(len(items_meta_data)):
for r in range(2):
np.savetxt(simu_dir+'/qlT_{}_{}.dat'.format(r,items_meta_data[meta]),q_l_taus[meta][:,:,r])
np.savetxt(simu_dir+'/zeta_{}.dat'.format(items_meta_data[meta]),zetes[meta])
for meta in range(len(N_att_meta_items)):
np.savetxt(simu_dir+'/q_ka_{}.dat'.format(node_meta_data[meta]),q_kas[meta])'''
#print(theta,eta,p_kl,omega,q_kas,omega_nodes,zetes,q_l_taus,omega_items)
return theta,eta,p_kl,omega,q_kas,omega_nodes,zetes,q_l_taus,omega_items
# In[118]:
@print_n_func
#@timer
@jit(nopython=True,parallel=True,locals=dict(i=int64,j=int64,k=int64,l=int64,rating=int64,link=int64,suma=double))
def log_like_comp_arrays(p_kl,eta,theta,K,L,links_array,links_ratings):
log_like = 0
for link in prange(len(links_ratings)):
i = links_array[link][0]
j = links_array[link][1]
rating = links_ratings[link]
suma = 0
for k in range(K):
for l in range(L):
suma += theta[i,k]*eta[j,l]*p_kl[k,l,rating]
log_like += np.log(suma)
return log_like
# In[119]:
@print_n_func
#@timer
@jit(nopython=True,parallel=True)
def log_like_comp_arrays_exclusive(theta,q_ka,K,links_array):
log_like = 0
for link in range(len(links_array)):
i = links_array[link][0]
a = links_array[link][1]
suma = 0
for k in range(K):
suma += theta[i,k]*q_ka[k,a]
#if suma<1.0e-16:
# print(i,veins_nodes_array[i],theta[i,:],q_ka[:,a])
log_like += np.log(suma)
return log_like
def load_matrix_simu(dir_matrix,K,L,N_ratings,items_meta_data,Taus,node_meta_data,N_att_meta_items):
eta = np.loadtxt('{}/eta.dat'.format(dir_matrix))
theta = np.loadtxt('{}/theta.dat'.format(dir_matrix))
p_kl = np.zeros((K,L,N_ratings))
for r in range(N_ratings):
p_kl[:,:,r] = np.loadtxt('{}/pkl_{}.dat'.format(dir_matrix,r))
omega = np.zeros((N_nodes,N_items,K,L),dtype=np.double)
omega = omega_comp_arrays(omega,p_kl,eta,theta,K,L,links_array,links_ratings)
q_l_taus = []
zetes = []
q_kas = []
omega_items = []
for meta in range(len(items_meta_data)):
q_l_taus.append(np.zeros((L,Taus[meta],N_att_meta_items[meta])))
for r in range(2):
q_l_taus[-1][:,:,r] = np.loadtxt('{}/qlT_{}_{}.dat'.format(dir_matrix,r,items_meta_data[meta]))
zetes.append(np.loadtxt('{}/zeta_{}.dat'.format(dir_matrix,items_meta_data[meta])))
omega_items.append(np.zeros((N_items,N_att_meta_items[meta],L,Taus[meta]),dtype=np.double))
omega_items[-1] = omega_comp_arrays(omega_items[-1],q_l_taus[-1],zetes[-1],eta,L,Taus[meta],metas_links_arrays_items[meta],metas_links_arrays_items_type[meta])
omega_nodes = []
for meta in range(len(node_meta_data)):
q_kas.append(np.loadtxt('{}/q_ka_{}.dat'.format(dir_matrix,node_meta_data[meta])))
omega_nodes.append(np.zeros((N_nodes,K),dtype=np.double))
omega_nodes[-1] = omega_comp_arrays_exclusive(omega_nodes[-1],q_kas[-1],theta,K,metas_links_arrays_nodes[meta])
return eta,theta,p_kl,q_l_taus,zetes,q_kas,omega,omega_items,omega_nodes
# In[132]:
print('ini')
N_ratings,links_array,links_ratings,links_by_ratings_array,veins_nodes_array,veins_items_array,N_veins_nodes,N_veins_items = obtain_links_arrays(node_header,item_header,lambda_nodes,lambda_items,rating_header)
# In[ ]:
N_run = 'prueba/'
direct = '.'
#BUCLE AQUI
if seed!=None:
np.random.seed(int(seed))
theta,eta,p_kl,omega,q_kas,omega_nodes,zetes,q_l_taus,omega_items = inicialitzacio(K,L,Taus,N_nodes,N_items,N_ratings,N_att_meta_nodes,N_att_meta_items,links_array,links_ratings,metas_links_arrays_nodes,metas_links_arrays_items)
print('UEP!!!')
# In[44]:
## date and time representation
file_info = open(simu_dir+'/info_simus.info','w')
file_info.write("Simulation started at:" + time_lib.strftime("%c")+'\n\n')
file_info.write('With parameters:\nK={}\nL={}\nN_nodes={}\nN_items={}\nN_ratings={}\nLinks_observed={}\nSeed={}\n\n############################\n'.format(K,L,N_nodes,N_items,N_ratings,N_links,seed))
file_info.write('Prior metadatas of nodes:\n')
if node_meta_data != None:
for meta in node_meta_data:
file_info.write('\t{}\n'.format(meta))
file_info.write('Prior metadatas of items:\n')
if items_meta_data != None:
for meta in items_meta_data:
file_info.write('\t{}\n'.format(meta))
file_info.write('Prior coupling constants:\n')
file_info.write('\tNodes: {}\n'.format(lambda_nodes))
file_info.write('-Items categories:\n')
file_info.write('\tItems: {}\n'.format(lambda_items))
file_info.close()
tik_simu = time()
#theta,eta,p_kl,omega,q_ka_ages,omega_ages,q_ka_genders,omega_genders,zeta,q_l_tau,omega_genres = inicialitzacio(K,L,Tau,N_nodes,N_items,N_ratings,N_ages,N_genres,N_genders,links_array,links_ratings,genre_link_array,age_link_array,gender_link_array,link_genre)
#eta,theta,p_kl,q_l_taus,zetes,q_kas,omega,omega_items,omega_nodes = load_matrix_simu('input_matrix',K,L,N_ratings,items_meta_data,Taus,node_meta_data,N_att_meta_items)
file_logLike = open(simu_dir+'/log_evolution.dat'.format(N_run),'w')
file_logLike.write("iteration\tlog_likelihood\tlog_prior_nodes\tlog_prior_items\tlog_posterior\tposterior_variation\n")
old_log_like = 0.0
old_log_like += log_like_comp_arrays(p_kl,eta,theta,K,L,links_array,links_ratings)
for meta in range(len(node_meta_data)):
old_log_like += lambda_nodes*log_like_comp_arrays_exclusive(theta,q_kas[meta],K,metas_links_arrays_nodes[meta])
for meta in range(len(items_meta_data)):
old_log_like += lambda_items*log_like_comp_arrays(q_l_taus[meta],zetes[meta],eta,L,Taus[meta],metas_links_arrays_items[meta],metas_links_arrays_items_type[meta])
# In[109]:
print('copy')
theta_old = theta.copy()
eta_old = eta.copy()
theta_temp = theta.copy()
eta_temp = eta.copy()
p_kl_old = p_kl.copy()
zetes_old = deepcopy(zetes)
q_kas_old = deepcopy(q_kas)
q_l_taus_old = deepcopy(q_l_taus)
# In[ ]:
print('simu',N_itt,N_measure)
for itt in range(N_itt):
theta = theta_comp_arrays_multilayer(omega_nodes,omega,theta,K,observed_nodes,nodes_no_observed,np.array(N_veins_nodes)[:,np.newaxis])
for meta in range(len(node_meta_data)):
q_kas[meta] = q_ka_comp_arrays(omega_nodes[meta],q_kas[meta],K,metas_links_arrays_nodes[meta],N_veins_metas_nodes[meta])
#print(itt,q_kas[meta].sum())
omega_nodes[meta] = omega_comp_arrays_exclusive(omega_nodes[meta],q_kas[meta],theta,N_nodes,metas_links_arrays_nodes[meta])
'''if itt>-1:
print(itt)
#print(theta)
tmp = theta_comp_arrays_exclusive(omega_ages,theta,K,age_link_array,N_veins_nodes)
for i in tmp:
print(i)'''
N_om = 0
for meta,n in enumerate(N_att_meta_items):
N_om += n*L*N_items*Taus[meta]
omega_items_flated = np.zeros(N_om)
start = 0
for meta,o in enumerate(omega_items):
omega_items_flated[start:N_att_meta_items[meta]*L*N_items*Taus[meta]+start] = o.flatten()