def test_sync_gosta(nb_iter = 3000, trace_period = 10,
data_file = 'wine.csv', graph_type = 'watts',
pairwise_f = pf.within_clust_scatter,
averaging_function = gosta.pair_avg,
graph = None, data = None, **kwargs):
if(graph == None or data == None):
data = gc.parse(data_file)
graph = gc.build_graph(data, graph_type, **kwargs)
trace = gosta.gosta_sync(graph, pairwise_f, nb_iter,
trace_period = trace_period,
averaging_function = averaging_function)
results = [graph.node[n]['estimate'] for n in graph]
true_mean = compute_truth_all_pairs(data, pairwise_f)
if (trace_period != None):
plt.figure(1)
plt.plot(trace)
plt.plot(true_mean*np.ones(nb_iter/trace_period-1))
plt.title(data_file + ', ' + graph_type)
return ({'gosta_mean' : np.mean(results),
'gosta_variance' : np.var(results),
'data_mean' : true_mean,
'graph' : graph}, trace)
def gosta_experiment(nb_repetitions = 1, n_iter = 10000, trace_period = 10,
data_files = ['wine.csv'],
graph_type_list = ['watts', 'complete', 'grid'],
f_to_avg = pf.within_clust_scatter,
averaging_function = gosta.neighbourhood_avg,
root_results_folder = path_to_results_folder()):
for data_src in data_files:
data_name = re.match(r'([^\.]+)\..*', data_src).group(1)
print '\n', data_name
data = gc.parse(data_src)
print data.shape
true_mean = compute_truth_all_pairs(
data, f_to_avg)
for graph_type in graph_type_list:
print '\n', graph_type, '\n'
results_folder_name = '%s_%s_%s' % (
function_names[averaging_function], data_name, graph_type)
results_folder = utils.make_new_dir(
root_results_folder, results_folder_name)
graph = gc.build_graph(data, graph_type)
traces = []
error_traces = []
for rep in range(nb_repetitions):
print '%s, %s, repetition %d' % (data_name, graph_type, rep)
trace = gosta.gosta_sync(
graph, f_to_avg, n_iter = n_iter,
trace_period = trace_period,
averaging_function = averaging_function,
log_filename = path.join(results_folder, 'log.npy'),
log_period = 1000)
traces.append(trace)
error_traces.append(np.abs((np.array(trace)
- true_mean)
/ true_mean))
traces = np.array(traces)
error_traces = np.atleast_2d(np.array(error_traces))
err_mean = np.mean(error_traces, axis = 0)
traces_file_name = path.join(
results_folder,
'%s_%s_traces.npy' % (data_name, graph_type))
mean_file_name = path.join(
results_folder,
'%s_%s_mean.npy' % (data_name, graph_type))
with open(traces_file_name, 'w') as traces_file:
np.save(traces_file, traces)
with open(mean_file_name, 'w') as mean_file:
np.save(mean_file, err_mean)
