def init_data_input(params, transformer_path):
data_input = DataInput(params['data_params'])
data_input.split_data()
with open(transformer_path, 'rb') as f:
data_transformer = pickle.load(f)
print(data_transformer)
data_input.embed_data(data_transformer, \
params['transform_params']['cells_to_subsample'],
params['transform_params']['num_cells_for_transformer']
)
data_input.normalize_data()
data_input.prepare_data_for_training()
return data_input
def main(path_to_params):
start_time = time.time()
params = TransformParameterParser(path_to_params).parse_params()
print(params)
if not os.path.exists(params['save_dir']):
os.makedirs(params['save_dir'])
with open(os.path.join(params['save_dir'], 'params.pkl'), 'wb') as f:
pickle.dump(params, f)
data_input = DataInput(params['data_params'])
data_input.split_data()
data_transformer = DataTransformerFactory(
params['transform_params'],
params['random_seed']).manufacture_transformer()
data_input.embed_data_and_fit_transformer(\
data_transformer,
params['transform_params']['cells_to_subsample'],
params['transform_params']['num_cells_for_transformer'])
data_input.save_transformer(params['save_dir'])
data_input.normalize_data()
init_gate_tree = init_plot_and_save_gates(data_input, params)
model = initialize_model(params['model_params'], init_gate_tree)
data_input.prepare_data_for_training()
performance_tracker = run_train_model(model, params['train_params'],
data_input)
model_save_path = os.path.join(params['save_dir'], 'model.pkl')
torch.save(model.state_dict(), model_save_path)
tracker_save_path = os.path.join(params['save_dir'], 'tracker.pkl')
with open(tracker_save_path, 'wb') as f:
pickle.dump(performance_tracker, f)
results_plotter = DataAndGatesPlotterDepthOne(
model, np.concatenate(data_input.x_tr))
#fig, axes = plt.subplots(params['gate_init_params']['n_clusters'], figsize=(1 * params['gate_init_params']['n_clusters'], 3 * params['gate_init_params']['n_clusters']))
results_plotter.plot_data_with_gates(
np.array(
np.concatenate([
data_input.y_tr[i] *
torch.ones([data_input.x_tr[i].shape[0], 1])
for i in range(len(data_input.x_tr))
])))
plt.savefig(os.path.join(params['save_dir'], 'final_gates.png'))
print('Complete main loop took %.4f seconds' % (time.time() - start_time))
def main(path_to_params):
start_time = time.time()
params = TransformParameterParser(path_to_params).parse_params()
print(params)
check_consistency_of_params(params)
set_random_seeds(params)
if not os.path.exists(params['save_dir']):
os.makedirs(params['save_dir'])
with open(os.path.join(params['save_dir'], 'params.pkl'), 'wb') as f:
pickle.dump(params, f)
data_input = DataInput(params['data_params'])
data_input.split_data()
data_transformer = DataTransformerFactory(
params['transform_params'],
params['random_seed']).manufacture_transformer()
data_input.embed_data_and_fit_transformer(\
data_transformer,
params['transform_params']['cells_to_subsample'],
params['transform_params']['num_cells_for_transformer']
)
data_input.save_transformer(params['save_dir'])
data_input.normalize_data()
#everything below differs from the other main_UMAP
multi_gate_initializer = MultipleGateInitializerHeuristic(
data_input, params['model_params']['node_type'],
params['gate_init_multi_heuristic_params'])
init_gate_tree = [multi_gate_initializer.init_next_gate()]
model = initialize_model(params['model_params'], init_gate_tree)
data_input.prepare_data_for_training()
trackers_per_step = []
num_gates = params['gate_init_multi_heuristic_params']['num_gates']
for i in range(num_gates):
performance_tracker = run_train_model(model, params['train_params'],
data_input)
multi_gate_initializer.gates = model.get_gates()
if not (i == num_gates - 1):
print(model.get_gates())
next_gate = multi_gate_initializer.init_next_gate()
if next_gate is None:
print(
'There are no non-overlapping initializations left to try!'
)
break
model.add_node(next_gate)
model_save_path = os.path.join(params['save_dir'], 'model.pkl')
torch.save(model.state_dict(), model_save_path)
tracker_save_path = os.path.join(params['save_dir'], 'tracker.pkl')
with open(tracker_save_path, 'wb') as f:
pickle.dump(performance_tracker, f)
results_plotter = DataAndGatesPlotterDepthOne(
model, np.concatenate(data_input.x_tr))
#fig, axes = plt.subplots(params['gate_init_params']['n_clusters'], figsize=(1 * params['gate_init_params']['n_clusters'], 3 * params['gate_init_params']['n_clusters']))
results_plotter.plot_data_with_gates(
np.array(
np.concatenate([
data_input.y_tr[i] *
torch.ones([data_input.x_tr[i].shape[0], 1])
for i in range(len(data_input.x_tr))
])))
plt.savefig(os.path.join(params['save_dir'], 'final_gates.png'))
print('Complete main loop took %.4f seconds' % (time.time() - start_time))