def train_custom(conf, plot_path, debug, verbose, gs_params=None, callbacks=default_callbacks):
global config, plot, train_errors, valid_errors
plot = plot_path
train_errors = np.zeros(conf['epochs'])
valid_errors = np.zeros(conf['epochs'])
all_ = list()
for x in ['all', 'md', 'mir', 'feat_sel', 'random']:
all = list()
if x in ('all', 'md', 'mir'):
training_data, training_targets, valid_data, valid_targets = getData(conf['datasets'], 0, None,
None, type=x)
elif x == 'random':
from utils import features
import random
conf['features'] = random.sample(np.hstack(features.values()), random.randint(1, 115))
training_data, training_targets, valid_data, valid_targets = getData(conf['datasets'], 0, conf['features'],
balanced=conf['balanced'], type=x)
else:
training_data, training_targets, valid_data, valid_targets = getData(conf['datasets'], 0, conf['features'],
balanced=conf['balanced'], type=x)
# if there is not enough data available
conf['datasets'] = training_data.shape[0]
if conf['units'] is None:
conf['units'] = [int(math.ceil((training_data.shape[1] + 7) / 2))]
conf['n_input'] = training_data.shape[1]
config = conf
units = [int(math.ceil((training_data.shape[1] + 7) / 2))]
for i in range(1, 101):
net = getNet(units, conf['learning_rate'], conf['epochs'], conf['learning_rule'],
conf['batch_size'], conf['weight_decay'], conf['dropout_rate'],
conf['loss_type'], n_stable=conf['n_stable'], debug=debug, verbose=verbose,
callbacks=callbacks,
# valid_set=(valid_data, valid_targets)
valid_size=conf['ratio']
)
pipeline = learning_utils.getPipeline(training_data, net)
pipeline.fit(training_data, training_targets)
all.append(valid_errors)
all_.append(np.array(all).mean(axis=0))
learning_utils.plot_lines(data=all_, labels=["all", "md", "mir", "feat_sel", "random"],
xlabel="number of epochs",
ylabel=config['loss_type'],
title="mean training and validation error", suptitle=None,
path="learning/nn/plots/comb/test.png")
i0 = options.md_features
elif options.type == 'feat_sel':
if options.pickle_file:
features = None
# get feature list
features = joblib.load(options.pickle_file)
i0 = len(features)
else:
print "Please specify the location of the pickle file (-p) containing the list of features"
sys.exit(2)
elif options.type == 'rand':
from utils import features
import random
features = random.sample(
np.hstack(features.values()),
random.randint(1, options.total_features))
i0 = len(features)
else:
i0 = total_features
if options.units is None and unit_range is None:
units = [int(math.ceil((i0 + 7) / 2))]
if options.plot_path:
if options.plot_path == 'gen':
options.plot_path = os.path.join(
os.getcwd(), 'learning', 'nn', 'plots', 'units',
"{}_{}_{}_{}_{}_{}_{}_{}_{}.png".format(
options.type, options.units, options.n_iter,
options.learning_rate, options.batch_size,
options.weight_decay, options.dropout_rate,
elif options.type == 'md':
i0 = options.md_features
elif options.type == 'feat_sel':
if options.pickle_file:
features = None
# get feature list
features = joblib.load(options.pickle_file)
i0 = len(features)
else:
print "Please specify the location of the pickle file (-p) containing the list of features"
sys.exit(2)
elif options.type == 'rand':
from utils import features
import random
features = random.sample(np.hstack(features.values()), random.randint(1, options.total_features))
i0 = len(features)
else:
i0 = total_features
if options.units is None and unit_range is None:
units = [int(math.ceil((i0 + 7) / 2))]
if options.plot_path:
if options.plot_path == 'gen':
options.plot_path = os.path.join(os.getcwd(), 'learning', 'nn', 'plots',
'units',
"{}_{}_{}_{}_{}_{}_{}_{}_{}.png".format(options.type,
options.units,
options.n_iter,
options.learning_rate,
options.batch_size,
