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cv_eval.py
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cv_eval.py
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import time
import logging
from functions import *
from nrlmf import NRLMF
from netlaprls import NetLapRLS
from blm import BLMNII
from wnngip import WNNGIP
#from kbmf import KBMF
from cmf import CMF
from sklearn.gaussian_process import GaussianProcessRegressor
def nrlmf_cv_eval(method,dataset,cv_data,intMat,Kd,Kt,cvs,para,logger,scoring='auc',gpmi=None):
# GP-MI (Bayesian optimization)
if gpmi is not None:
# Generate parameters
params_grid, x_grid = list(), list()
for r in [50, 100]:
for x in np.arange(-5, 2):
for y in np.arange(-5, 3):
for z in np.arange(-5, 1):
for t in np.arange(-3, 1):
params_grid.append({'cfix':para['c'],'K1':para['K1'],'K2':para['K2'],'num_factors':r,'lambda_d':2.**(x),'lambda_t':2.**(x),'alpha':2.**(y),'beta':2.**(z),'theta':2.**(t),'max_iter':100})
x_grid.append([para['c'],para['K1'],para['K2'],r,2.**(x),2.**(x),2.**(y),2.**(z),2.**(t),100])
# Initialization
start = time.time()
n_init = int(gpmi['n_init']) if gpmi['n_init'] > 0 else 1
best_score = 0
count = 1
if n_init > 0:
np.random.seed(list(cv_data.keys())[0])
i_init = np.random.permutation(range(len(params_grid)))[0:n_init]
X = np.array([x_grid[i] for i in i_init])
y = np.array(list())
for i in i_init:
tic = time.clock()
params_next = params_grid[i]
model = NRLMF(**params_next)
aupr_vec, auc_vec = train(model, cv_data, intMat, Kd, Kt)
aupr_avg, aupr_conf = mean_confidence_interval(aupr_vec)
auc_avg, auc_conf = mean_confidence_interval(auc_vec)
y_next = auc_avg if scoring=='auc' else aupr_avg
y = np.concatenate((y,[y_next]),axis=0)
logger.info("%s %s cvs=%s (sample= %s) %.6f[sec]" % (params_grid[i],scoring,str(cvs),str(count),time.clock()-tic))
logger.info(str(y_next))
if i == 0:
cmd = "Dataset:"+dataset+" CVS: "+str(cvs)+"\n"+str(model)
best_params, best_score = params_grid[i], y_next
auc_opt = [cmd, auc_avg, aupr_avg, auc_conf, aupr_conf]
if best_score < y_next:
cmd = "Dataset:"+dataset+" CVS: "+str(cvs)+"\n"+str(model)
best_params, best_score = params_grid[i], y_next
auc_opt = [cmd, auc_avg, aupr_avg, auc_conf, aupr_conf]
count += 1
# GP-MI algorithm
alpha = np.log(2/gpmi['delta'])
gamma = 0
for i in range(int(gpmi['max_iter'])):
tic = time.clock()
gp = GaussianProcessRegressor()
gp.fit(X,y)
mean, sig = gp.predict(x_grid,return_std=True)
phi = np.sqrt(alpha) * (np.sqrt(sig**2+gamma)-np.sqrt(gamma))
idx = np.argmax(mean+phi)
params_next = params_grid[idx]
x_next = x_grid[idx]
gamma = gamma + sig[idx]**2
model = NRLMF(**params_next)
aupr_vec, auc_vec = train(model, cv_data, intMat, Kd, Kt)
aupr_avg, aupr_conf = mean_confidence_interval(aupr_vec)
auc_avg, auc_conf = mean_confidence_interval(auc_vec)
y_next = auc_avg if scoring=='auc' else aupr_avg
logger.info("%s %s cvs=%s (sample= %s) %.6f[sec]" % (params_grid[i],scoring,str(cvs),str(i+n_init+1),time.clock()-tic))
logger.info(str(y_next))
if best_score < y_next:
cmd = "Dataset:"+dataset+" CVS: "+str(cvs)+"\n"+str(model)
best_params, best_score = params_next, y_next
auc_opt = [cmd, auc_avg, aupr_avg, auc_conf, aupr_conf]
if np.array_equal(x_next,X[-1]): break
X = np.concatenate((X,[x_next]),axis=0)
y = np.concatenate((y,[y_next]),axis=0)
end = time.time()
cmd = "Optimal parameter setting:\n%s\n" % auc_opt[0]
cmd += "auc: %.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f, time:%.6f\n" % (auc_opt[1], auc_opt[2], auc_opt[3], auc_opt[4], end-start)
# Grid search
else:
start = time.clock()
max_auc, auc_opt = 0, []
for r in [50, 100]:
for x in np.arange(-5, 2):
for y in np.arange(-5, 3):
for z in np.arange(-5, 1):
for t in np.arange(-3, 1):
tic = time.clock()
model = NRLMF(cfix=para['c'], K1=para['K1'], K2=para['K2'], num_factors=r, lambda_d=2.**(x), lambda_t=2.**(x), alpha=2.**(y), beta=2.**(z), theta=2.**(t), max_iter=100)
cmd = "Dataset:"+dataset+" CVS: "+str(cvs)+"\n"+str(model)
logger.info(cmd)
aupr_vec, auc_vec = train(model, cv_data, intMat, Kd, Kt)
aupr_avg, aupr_conf = mean_confidence_interval(aupr_vec)
auc_avg, auc_conf = mean_confidence_interval(auc_vec)
logger.info("auc:%.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f, Time:%.6f\n" % (auc_avg, aupr_avg, auc_conf, aupr_conf, time.clock()-tic))
if auc_avg > max_auc:
max_auc = auc_avg
auc_opt = [cmd, auc_avg, aupr_avg, auc_conf, aupr_conf]
end = time.clock()
cmd = "Optimal parameter setting:\n%s\n" % auc_opt[0]
cmd += "auc: %.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f, time:%.6f\n" % (auc_opt[1], auc_opt[2], auc_opt[3], auc_opt[4], end-start)
logger.info('')
logger.info(cmd)
def netlaprls_cv_eval(method, dataset, cv_data, X, D, T, cvs, para):
max_auc, auc_opt = 0, []
for x in np.arange(-6, 3): # [-6, 2]
for y in np.arange(-6, 3): # [-6, 2]
tic = time.clock()
model = NetLapRLS(gamma_d=10**(x), gamma_t=10**(x), beta_d=10**(y), beta_t=10**(y))
cmd = "Dataset:"+dataset+" CVS: "+str(cvs)+"\n"+str(model)
print(cmd)
aupr_vec, auc_vec = train(model, cv_data, X, D, T)
aupr_avg, aupr_conf = mean_confidence_interval(aupr_vec)
auc_avg, auc_conf = mean_confidence_interval(auc_vec)
print("auc:%.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f, Time:%.6f\n" % (auc_avg, aupr_avg, auc_conf, aupr_conf, time.clock()-tic))
if auc_avg > max_auc:
max_auc = auc_avg
auc_opt = [cmd, auc_avg, aupr_avg, auc_conf, aupr_conf]
cmd = "Optimal parameter setting:\n%s\n" % auc_opt[0]
cmd += "auc: %.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f\n" % (auc_opt[1], auc_opt[2], auc_opt[3], auc_opt[4])
print(cmd)
def blmnii_cv_eval(method, dataset, cv_data, X, D, T, cvs, para):
max_auc, auc_opt = 0, []
for x in np.arange(0, 1.1, 0.1):
tic = time.clock()
model = BLMNII(alpha=x, avg=False)
cmd = "Dataset:"+dataset+" CVS: "+str(cvs)+"\n"+str(model)
print(cmd)
aupr_vec, auc_vec = train(model, cv_data, X, D, T)
aupr_avg, aupr_conf = mean_confidence_interval(aupr_vec)
auc_avg, auc_conf = mean_confidence_interval(auc_vec)
print("auc:%.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f, Time:%.6f\n" % (auc_avg, aupr_avg, auc_conf, aupr_conf, time.clock()-tic))
if auc_avg > max_auc:
max_auc = auc_avg
auc_opt = [cmd, auc_avg, aupr_avg, auc_conf, aupr_conf]
cmd = "Optimal parameter setting:\n%s\n" % auc_opt[0]
cmd += "auc: %.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f\n" % (auc_opt[1], auc_opt[2], auc_opt[3], auc_opt[4])
print(cmd)
def wnngip_cv_eval(method, dataset, cv_data, X, D, T, cvs, para):
max_auc, auc_opt = 0, []
for x in np.arange(0.1, 1.1, 0.1):
for y in np.arange(0.0, 1.1, 0.1):
tic = time.clock()
model = WNNGIP(T=x, sigma=1, alpha=y)
cmd = "Dataset:"+dataset+" CVS: "+str(cvs)+"\n"+str(model)
print(cmd)
aupr_vec, auc_vec = train(model, cv_data, X, D, T)
aupr_avg, aupr_conf = mean_confidence_interval(aupr_vec)
auc_avg, auc_conf = mean_confidence_interval(auc_vec)
print("auc:%.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f, Time:%.6f\n" % (auc_avg, aupr_avg, auc_conf, aupr_conf, time.clock()-tic))
if auc_avg > max_auc:
max_auc = auc_avg
auc_opt = [cmd, auc_avg, aupr_avg, auc_conf, aupr_conf]
cmd = "Optimal parameter setting:\n%s\n" % auc_opt[0]
cmd += "auc: %.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f\n" % (auc_opt[1], auc_opt[2], auc_opt[3], auc_opt[4])
print(cmd)
def kbmf_cv_eval(method, dataset, cv_data, X, D, T, cvs, para):
max_auc, auc_opt = 0, []
for d in [50, 100]:
tic = time.clock()
model = KBMF(num_factors=d)
cmd = "Dataset:"+dataset+" CVS: "+str(cvs)+"\n"+str(model)
print(cmd)
aupr_vec, auc_vec = train(model, cv_data, X, D, T)
aupr_avg, aupr_conf = mean_confidence_interval(aupr_vec)
auc_avg, auc_conf = mean_confidence_interval(auc_vec)
print("auc:%.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f, Time:%.6f\n" % (auc_avg, aupr_avg, auc_conf, aupr_conf, time.clock()-tic))
if auc_avg > max_auc:
max_auc = auc_avg
auc_opt = [cmd, auc_avg, aupr_avg, auc_conf, aupr_conf]
cmd = "Optimal parameter setting:\n%s\n" % auc_opt[0]
cmd += "auc: %.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f\n" % (auc_opt[1], auc_opt[2], auc_opt[3], auc_opt[4])
print(cmd)
def cmf_cv_eval(method, dataset, cv_data, X, D, T, cvs, para):
max_aupr, aupr_opt = 0, []
for d in [50, 100]:
for x in np.arange(-2, -1):
for y in np.arange(-3, -2):
for z in np.arange(-3, -2):
tic = time.clock()
model = CMF(K=d, lambda_l=2.**(x), lambda_d=2.**(y), lambda_t=2.**(z), max_iter=30)
cmd = "Dataset:"+dataset+" CVS: "+str(cvs)+"\n"+str(model)
print(cmd)
aupr_vec, auc_vec = train(model, cv_data, X, D, T)
aupr_avg, aupr_conf = mean_confidence_interval(aupr_vec)
auc_avg, auc_conf = mean_confidence_interval(auc_vec)
print("auc:%.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f, Time:%.6f\n" % (auc_avg, aupr_avg, auc_conf, aupr_conf, time.clock()-tic))
if aupr_avg > max_aupr:
max_aupr = aupr_avg
aupr_opt = [cmd, auc_avg, aupr_avg, auc_conf, aupr_conf]
cmd = "Optimal parameter setting:\n%s\n" % aupr_opt[0]
cmd += "auc: %.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f\n" % (aupr_opt[1], aupr_opt[2], aupr_opt[3], aupr_opt[4])
print(cmd)