def collect_gcnn(name, most_similar_name, phi, ind):
gcnn_results = []
data = dataTools.AuthorshipOneVsOne(name, most_similar_name, ratioTrain,
ratioValid, dataPath)
logging.info('Training GCNN on {0} v.s. {1}'.format(
name, most_similar_name))
h_params = ClusterUtils.load_best_hyperparams(name)
for split_n in range(NO_OF_SPLITS):
data.get_split(name, most_similar_name, ratioTrain, ratioValid)
data.reduce_dim(ind)
start = time.perf_counter()
gcnn = train_helper.train_net(data, h_params, phi=phi)
end = time.perf_counter()
gcnn_eval = evaluate_gcnn(gcnn, data)
gcnn_eval['time'] = start - end
gcnn_results.append(gcnn_eval)
logging.info(
'SPLIT {0}: GCNN results successfully collected: {1}'.format(
split_n, gcnn_results[split_n]))
return gcnn_results
def collect_gcnn(name, most_similar_name, perc):
gcnn_results = []
data = dataTools.Authorship(name, ratioTrain, ratioValid, dataPath)
logging.info('Training GCNN on {0}'.format(name))
h_params = ClusterUtils.load_best_hyperparams(name)
phi, indices = load_phi(name, data, percentage=perc)
if indices.shape[0] < 2:
return [{'acc': 0, 'f1': 0, 'auc': 0, 'prec': 0, 'time': 0}]
for split_n in range(NO_OF_SPLITS):
data.get_split(name, ratioTrain, ratioValid)
data.reduce_dim(indices)
start = time.perf_counter()
gcnn = train_helper.train_net(data, h_params, phi=phi)
end = time.perf_counter()
gcnn_eval = evaluate_gcnn(gcnn, data)
gcnn_eval['time'] = start - end
gcnn_results.append(gcnn_eval)
logging.info(
'SPLIT {0}: GCNN results successfully collected: {1}'.format(
split_n, gcnn_results[split_n]))
return gcnn_results
def collect_gcnn(name, most_similar_name):
gcnn_results = []
data = dataTools.AuthorshipOneVsOne(name, most_similar_name, ratioTrain, ratioValid, dataPath)
logging.info('Training GCNN on {0} v.s. {1}'.format(name, most_similar_name))
h_params = ClusterUtils.load_best_hyperparams(name)
for split_n in range(NO_OF_SPLITS):
data.get_split(name, most_similar_name, ratioTrain, ratioValid)
gcnn = train_helper.train_net(data, h_params)
gcnn_results.append(evaluate_gcnn(gcnn, data))
logging.info('SPLIT {0}: GCNN results successfully collected: {1}'.format(split_n, gcnn_results[split_n]))
return gcnn_results