def run_BD(i):
tf.reset_default_graph()
data = DataReader.read_BD()
ncells = configs[i]['cells']
learning_rate = configs[i]['lr']
group = configs[i]['g']
iters = configs[i]['iters']
model_path = configs[i]['model_path']
output_path = Paths.local_path + 'BD/rnn-opt-rand-init/' + 'run_' + str(
configs[i]['s']) + '/' + str(ncells) + 'cells/' + group + '/'
with LogFile(output_path, 'run.log'):
DLogger.logger().debug("group: " + str(group))
gdata = data.loc[data.diag == group]
ids = gdata['id'].unique().tolist()
dftr = pd.DataFrame({'id': ids, 'train': 'train'})
tdftr = pd.DataFrame({'id': ids, 'train': 'test'})
train, test = DataProcess.train_test_between_subject(
gdata, pd.concat((dftr, tdftr)),
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
train = DataProcess.merge_data(train)
DLogger.logger().debug("total points: " + str(get_total_pionts(train)))
worker = LSTMBeh(2, 0, n_cells=ncells)
OptBEH.optimise(worker,
output_path,
train,
None,
learning_rate=learning_rate,
global_iters=iters,
load_model_path=model_path)
def run_BD(i):
tf.reset_default_graph()
data = DataReader.read_BD()
learning_rate = configs[i]['lr']
group = configs[i]['g']
output_path = Paths.local_path + 'BD/gql-ml-opt/' + group + '/'
with LogFile(output_path, 'run.log'):
DLogger.logger().debug("group: " + str(group))
gdata = data.loc[data.diag == group]
ids = gdata['id'].unique().tolist()
dftr = pd.DataFrame({'id': ids, 'train': 'train'})
tdftr = pd.DataFrame({'id': ids, 'train': 'test'})
train, test = DataProcess.train_test_between_subject(
gdata, pd.concat((dftr, tdftr)),
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
DLogger.logger().debug("total points: " + str(get_total_pionts(train)))
worker = GQL.get_instance(2, 2, {})
train = DataProcess.merge_data(train)
OptML.optimise(worker,
output_path,
train,
test,
global_iters=1000,
learning_rate=learning_rate)
def run_BD_RNN(i):
tf.reset_default_graph()
ncells = configs[i]['cells']
learning_rate = configs[i]['lr']
group = configs[i]['g']
cv_index = configs[i]['cv_index']
output_path = Paths.local_path + 'BD/rnn-cv/' + str(
ncells) + 'cells/' + group + '/' + 'fold' + str(cv_index) + '/'
with LogFile(output_path, 'run.log'):
indx_data = cv_lists_group[group][cv_index]
gdata = data.loc[data.diag == group]
train, test = DataProcess.train_test_between_subject(
gdata, indx_data, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
indx_data.to_csv(output_path + 'train_test.csv')
train_merged = DataProcess.merge_data(train)
DLogger.logger().debug("total points: " +
str(get_total_pionts(train_merged)))
del train
worker = LSTMBeh(2, 0, n_cells=ncells)
OptBEH.optimise(worker,
output_path,
train_merged,
None,
learning_rate=learning_rate,
global_iters=3000,
load_model_path='../inits/rnn-init/' + str(ncells) +
'cells/model-final/')
def run_BD_GQL(i):
tf.reset_default_graph()
learning_rate = configs[i]['lr']
group = configs[i]['g']
cv_index = configs[i]['cv_index']
iters = configs[i]['iters']
output_path = Paths.local_path + 'BD/gql-ml-rand-opt/' + group + '/' + 'fold' + str(
cv_index) + '/'
with LogFile(output_path, 'run.log'):
indx_data = cv_lists_group[group][cv_index]
gdata = data.loc[data.diag == group]
train, test = DataProcess.train_test_between_subject(
gdata, indx_data, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
indx_data.to_csv(output_path + 'train_test.csv')
DLogger.logger().debug("total points: " + str(get_total_pionts(train)))
worker = GQL.get_instance(2, 2, {})
train = DataProcess.merge_data(train)
OptML.optimise(worker,
output_path,
train,
None,
learning_rate=learning_rate,
global_iters=iters)
def run_BD_RNN(i):
tf.reset_default_graph()
ncells = configs[i]['cells']
lr = configs[i]['lr']
output_path = Paths.local_path + 'BD/rnn-init/' + str(ncells) + 'cells/'
with LogFile(output_path, 'run.log'):
ids = data['id'].unique().tolist()
dftr = pd.DataFrame({'id': ids, 'train': 'train'})
train, test = DataProcess.train_test_between_subject(
data, dftr, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
train = DataProcess.merge_data(train)
DLogger.logger().debug("total points: " + str(get_total_pionts(train)))
worker = LSTMBeh(2, 0, n_cells=ncells)
lrh.OptBEH.optimise(worker,
output_path,
train,
None,
learning_rate=lr,
global_iters=0)