model_name = None
models_names = [model_str for model_str in MODELS if model_str in arguments and arguments[model_str]]
if models_names and len(models_names) == 1:
model_name = models_names[0]
if not model_name:
print 'Please insert a valid model name'
exit(0)
ip_dir = arguments['--ip_dir']
rating_file = os.path.join(ip_dir, EP_RATING)
trust_file = os.path.join(ip_dir, EP_TRUST)
uik = int(arguments['--uik'])
uuk = int(arguments['--uuk'])
generator = DataGen(rating_file, trust_file, uik=uik, uuk=uuk)
generator.generate()
print 'User Item Matrix Size:', generator.ui_matrix.shape
print 'User User Matrix Size:', generator.uu_matrix.shape
results = None
threshold = None
if model_name == 'mf':
import matrixfac
K = int(arguments['--K'])
steps = int(arguments['--steps'])
alpha = float(arguments['--alpha'])
beta = float(arguments['--beta'])
threshold = float(arguments['--threshold'])
print 'Running MF with K=%d, steps=%d, threshold=%f, alpha=%f and beta=%f' % (K, steps, threshold, alpha, beta)
results = matrixfac.run_rec(generator, k=K, steps=steps, alpha=alpha, beta=beta)
print "RMSE: %f" % recstats.rmse(generator.ui_matrix, results)
print "Coverage: %f " % recstats.coverage(results, threshold if threshold else 1)
X = len(gen.ui_matrix[0])
P = np.random.rand(N, k)
Q = np.random.rand(M, k)
R = uiu_matrix
nP, nQ = matrix_factorization(R, P, Q, k, steps=steps, alpha=alpha, beta=beta)
mf_results = np.dot(nP, nQ.T)
# trimming off the trust part of the matrix
mf_user_item_matrix = mf_results[0:W, 0:X]
return mf_user_item_matrix
if __name__ == '__main__':
import recstats
# current file directory, keep the mat files in the same place
ip_dir = os.getcwd()
rating_file = os.path.join(ip_dir, EP_RATING)
trust_file = os.path.join(ip_dir, EP_TRUST)
generator = DataGen(rating_file, trust_file)
ui_matrix = run_rec(generator, 100)
print "RMSE: %f Coverage: %f " % (recstats.rmse(generator.ui_matrix, ui_matrix), recstats.coverage(ui_matrix, 1))
print 'Please insert a valid model name'
exit(0)
ip_dir = arguments['--ip_dir']
rating_file = os.path.join(ip_dir, EP_RATING)
trust_file = os.path.join(ip_dir, EP_TRUST)
uik = int(arguments['--uik'])
uuk = int(arguments['--uuk'])
generator = DataGen(rating_file, trust_file, uik=uik, uuk=uuk)
generator.generate()
print 'User Item Matrix Size:', generator.ui_matrix.shape
print 'User User Matrix Size:', generator.uu_matrix.shape
results = None
threshold = None
if model_name == 'mf':
import matrixfac
K = int(arguments['--K'])
steps = int(arguments['--steps'])
alpha = float(arguments['--alpha'])
beta = float(arguments['--beta'])
threshold = float(arguments['--threshold'])
print 'Running MF with K=%d, steps=%d, threshold=%f, alpha=%f and beta=%f' % (
K, steps, threshold, alpha, beta)
results = matrixfac.run_rec(generator,
k=K,
steps=steps,
alpha=alpha,
beta=beta)
print "RMSE: %f" % recstats.rmse(generator.ui_matrix, results)
print "Coverage: %f " % recstats.coverage(results,
threshold if threshold else 1)