if not len(sys.argv) == 1:
leave_k_out = int(sys.argv[1])
print 'Use default leave k out: k=' + str(leave_k_out)
if not len(sys.argv) <= 2:
lafactor = int(sys.argv[2])
print 'Use default latent factor: ' + str(lafactor)
max_rank = 2000
# number of repetitions.
total_iteration = 2
# recommendation algorithms RandUV(latent_factor=lafactor),
method_list = [ HierLat(latent_factor=lafactor, cold_start = HierLat.CS_EQUAL_PROB), \
LMaFit(latent_factor=lafactor), NMF(latent_factor=lafactor),
PMF(latent_factor=lafactor), TriUHV(latent_factor=lafactor) ]
# main method.
result = experiment_coldstart_map(exp_name, daily_data_file, \
min_occ_user, min_occ_prog, num_user, num_prog,\
method_list, leave_k_out, total_iteration, max_rank, binary = False)
matlab_output = {}
for method_name, method_iter_perf in result.items():
print 'Method: ' + method_name
rmse = sum(x['RMSE'] for x in method_iter_perf) / len(method_iter_perf)
print '>>Average RMSE : %.5f' % rmse
perf_recall = np.zeros(len(method_iter_perf[0]['recall']))
perf_precision = np.zeros(len(method_iter_perf[0]['precision']))
exp_name = 'test_exp_mid_prec_rec'; # something meaningful.
# filtering criteria
min_occ_user = 35;
min_occ_prog = 300;
top_n = 50; # performance computed on top N;
leave_k_out = 10; # perform leave k out.
# number of repetitions.
total_iteration = 3;
# recommendation algorithms
method_list = [ LMaFit(latent_factor=lafactor), RandUV(latent_factor=lafactor), \
HierLat(latent_factor=lafactor), NMF(latent_factor=lafactor),
PMF(latent_factor=lafactor), TriUHV(latent_factor=lafactor) ];
# main method.
result = experiment_leave_k_out(exp_name, daily_data_file, min_occ_user, min_occ_prog, \
method_list, leave_k_out, total_iteration, top_n);
# display results (average RMSE).
for method_name, method_iter_perf in result.items():
print 'Method: '+ method_name;
print '>>Average precision : %.5f' % (sum( x['prec'] for x in method_iter_perf)/len(method_iter_perf));
print '>>Average recall : %.5f' % (sum( x['recall'] for x in method_iter_perf)/len(method_iter_perf));
print '>>Average rmse : %.5f' % (sum( x['rmse'] for x in method_iter_perf)/len(method_iter_perf));
#print method_iter_perf;
if not len(sys.argv) == 1:
leave_k_out = int(sys.argv[1]);
print 'Use default leave k out: k=' + str(leave_k_out);
if not len(sys.argv) <= 2:
lafactor = int(sys.argv[2]);
print 'Use default latent factor: ' + str(lafactor);
max_rank = 2000;
# number of repetitions.
total_iteration = 2;
# recommendation algorithms %RandUV(latent_factor=lafactor),
method_list = [ LMaFit(latent_factor=lafactor), item_item_sim(N = lafactor), \
HierLat(latent_factor=lafactor), NMF(latent_factor=lafactor),
PMF(latent_factor=lafactor), TriUHV(latent_factor=lafactor) ];
# main method.
result = experiment_leave_k_out_map(exp_name, daily_data_file, \
min_occ_user, min_occ_prog, num_user, num_prog,\
method_list, leave_k_out, total_iteration, max_rank, binary = True);
matlab_output = {};
for method_name, method_iter_perf in result.items():
print 'Method: '+ method_name;
rmse = sum( x['RMSE'] for x in method_iter_perf)/len(method_iter_perf);
print '>>Average RMSE : %.5f' % rmse;
perf_recall = np.zeros(len(method_iter_perf[0]['recall']));
filename = "../../../datasample/agg_duid_pid_watchtime_genre/20131209_100000"
# load data.
reader = DailyWatchTimeReader()
feedback_data = reader.read_file_with_minval(filename, 1, 1)
# build model with 3 latent factors.
r = 10
# the L_2 norm regularizer
lamb = 0.1
# the stopping delta value
delta = 1e-5
# the maximium iteration number
maxiter = 100
LMaFit_model = LMaFit(r, lamb, delta, maxiter, verbose=True)
LMaFit_model.train(feedback_data)
# test.
loc_row = [200, 4, 105]
loc_col = [10, 22, 4]
print 'Prediction:'
print LMaFit_model.predict(loc_row, loc_col)
# test cold start.
row = [0, 0, 1, 1, 2, 3, 3]
col = [0, 3, 1, 4, 0, 1, 3]
data = [1, 1, 1, 1, 1, 1, 1]
fbdata = FeedbackData(row, col, data, 4, 5, {}, {}, {})
LMaFit_model = LMaFit(2, 0.001, 1e-5, 100)
filename = "../../../datasample/agg_duid_pid_watchtime_genre/20131209_100000";
# load data.
reader = DailyWatchTimeReader();
feedback_data = reader.read_file_with_minval(filename, 1, 1);
# build model with 3 latent factors.
r = 10;
# the L_2 norm regularizer
lamb = 0.1;
# the stopping delta value
delta = 1e-5;
# the maximium iteration number
maxiter = 100;
LMaFit_model = LMaFit(r,lamb,delta,maxiter, verbose = True);
LMaFit_model.train(feedback_data);
# test.
loc_row = [200, 4, 105];
loc_col = [ 10, 22, 4];
print 'Prediction:'
print LMaFit_model.predict(loc_row, loc_col);
# test cold start.
row = [0, 0, 1, 1, 2, 3, 3];
col = [0, 3, 1, 4, 0, 1, 3];
data = [1, 1, 1, 1, 1, 1, 1];
fbdata = FeedbackData(row, col, data, 4, 5, {}, {}, {});
# filtering criteria
min_occ_user = 35
min_occ_prog = 300
# specify the percentage of training and (1 - training_prec) is testing.
training_prec = 0.5
# number of repetitions.
total_iteration = 5
# latent factor
lf = 10
# recommendation algorithms
method_list = [
LMaFit(latent_factor=lf),
RandUV(latent_factor=lf),
HierLat(latent_factor=lf)
]
# main method.
result = experiment_rand_split(exp_name, daily_data_file, min_occ_user, min_occ_prog, \
method_list, training_prec, total_iteration)
# display results (average RMSE).
for method_name, method_iter_perf in result.items():
print 'Method: ' + method_name
print '>>Average performance RMSE: %.5f' % (
sum(x for x in method_iter_perf) / len(method_iter_perf))
#print result;
exp_name = 'test_exp'
# something meaningful.
# filtering criteria
min_occ_user = 4
min_occ_prog = 1
# specify the percentage of training and (1 - training_prec) is testing.
training_prec = 0.5
# number of repetitions.
total_iteration = 3
# recommendation algorithms
method_list = [
LMaFit(latent_factor=2),
RandUV(latent_factor=2),
HierLat(latent_factor=2)
]
# main method.
result = experiment_rand_split(exp_name, daily_data_file, min_occ_user, min_occ_prog, \
method_list, training_prec, total_iteration)
# display results (average RMSE).
for method_name, method_iter_perf in result.items():
print 'Method: ' + method_name
print '>>Average performance RMSE: %.5f' % (
sum(x for x in method_iter_perf) / len(method_iter_perf))
#print result;