text: from collections import namedtuple
  import lasagne as nn
  import data_iterators
  import numpy as np
  import theano.tensor as T
  import nn_heart
  from configuration import subconfig
  import utils_heart
  import utils
  from pathfinder import PKL_TRAIN_DATA_PATH, TRAIN_LABELS_PATH, PKL_VALIDATE_DATA_PATH
  
  caching = None
  restart_from_save = None
  
  rng = subconfig().rng
  patch_size = subconfig().patch_size
  train_transformation_params = subconfig().train_transformation_params
  valid_transformation_params = subconfig().valid_transformation_params
  test_transformation_params = subconfig().test_transformation_params
  
  batch_size = 8
  nbatches_chunk = 2
  chunk_size = batch_size * nbatches_chunk
  
  train_valid_ids = utils.get_train_valid_split(PKL_TRAIN_DATA_PATH)
  
  train_data_iterator = data_iterators.PatientsDataGenerator(data_path=PKL_TRAIN_DATA_PATH,
                                                             batch_size=chunk_size,
                                                             transform_params=train_transformation_params,
                                                             patient_ids=train_valid_ids['train'],
                                                             labels_path=TRAIN_LABELS_PATH,
                                                             slice2roi_path='pkl_train_slice2roi.pkl',
                                                             full_batch=True, random=True, infinite=True, min_slices=5)
  
  valid_data_iterator = data_iterators.PatientsDataGenerator(data_path=PKL_TRAIN_DATA_PATH,
                                                             batch_size=chunk_size,
                                                             transform_params=valid_transformation_params,
                                                             patient_ids=train_valid_ids['valid'],
                                                             labels_path=TRAIN_LABELS_PATH,
                                                             slice2roi_path='pkl_train_slice2roi.pkl',
                                                             full_batch=False, random=False, infinite=False,
                                                             min_slices=5)
  
  test_data_iterator = data_iterators.PatientsDataGenerator(data_path=PKL_VALIDATE_DATA_PATH,
                                                            batch_size=chunk_size,
                                                            transform_params=test_transformation_params,
                                                            slice2roi_path='pkl_validate_slice2roi.pkl',
                                                            full_batch=False, random=False, infinite=False, min_slices=5)
  
  # find maximum number of slices
  nslices = max(train_data_iterator.nslices,
                valid_data_iterator.nslices,
                test_data_iterator.nslices)
  
  # set this max for every data iterator
  train_data_iterator.nslices = nslices
  valid_data_iterator.nslices = nslices
  test_data_iterator.nslices = nslices
  
  nchunks_per_epoch = train_data_iterator.nsamples / chunk_size
  max_nchunks = nchunks_per_epoch * 150
  learning_rate_schedule = {
      0: 0.0002,
      int(max_nchunks * 0.1): 0.0001,
      int(max_nchunks * 0.3): 0.00008,
      int(max_nchunks * 0.5): 0.00006,
      int(max_nchunks * 0.75): 0.00004,
      int(max_nchunks * 0.9): 0.00001,
      int(max_nchunks * 0.95): 0.000005
  }
  validate_every = nchunks_per_epoch
  save_every = nchunks_per_epoch
  
  
  def build_model():
      l_in = nn.layers.InputLayer((None, nslices, 30) + patch_size)
      l_in_slice_mask = nn.layers.InputLayer((None, nslices))
      l_in_slice_location = nn.layers.InputLayer((None, nslices, 1))
      l_in_sex_age = nn.layers.InputLayer((None, 2))
      l_ins = [l_in, l_in_slice_mask, l_in_slice_location, l_in_sex_age]
  
      l_in_rshp = nn.layers.ReshapeLayer(l_in, (-1, 30) + patch_size)  # (batch_size*nslices, 30, h,w)
      submodel = subconfig().build_model(l_in_rshp)
  
      # ------------------ systole
      l_mu0 = submodel.mu_layers[0]
      l_sigma0 = submodel.sigma_layers[0]
  
      l_mu0 = nn.layers.ReshapeLayer(l_mu0, (-1, nslices, [1]))
      l_sigma0 = nn.layers.ReshapeLayer(l_sigma0, (-1, nslices, [1]))
  
      l_volume_mu_sigma0 = nn_heart.JeroenLayer([nn.layers.flatten(l_mu0), nn.layers.flatten(l_sigma0),
                                                 l_in_slice_mask, nn.layers.flatten(l_in_slice_location)],
                                                trainable_scale=False)
  
      l_volume_mu0 = nn.layers.SliceLayer(l_volume_mu_sigma0, indices=0, axis=-1)
      l_volume_sigma0 = nn.layers.SliceLayer(l_volume_mu_sigma0, indices=1, axis=-1)
  
      l_cdf0 = nn_heart.NormalCDFLayer(nn.layers.reshape(l_volume_mu0, ([0], 1)),
                                       nn.layers.reshape(l_volume_sigma0, ([0], 1)))
  
      # ------------------ diastole
      l_mu1 = submodel.mu_layers[1]
      l_sigma1 = submodel.sigma_layers[1]
  
      l_mu1 = nn.layers.ReshapeLayer(l_mu1, (-1, nslices, [1]))
      l_sigma1 = nn.layers.ReshapeLayer(l_sigma1, (-1, nslices, [1]))
  
      l_volume_mu_sigma1 = nn_heart.JeroenLayer([nn.layers.flatten(l_mu1), nn.layers.flatten(l_sigma1),
                                                 l_in_slice_mask, nn.layers.flatten(l_in_slice_location)],
                                                trainable_scale=False)
  
      l_volume_mu1 = nn.layers.SliceLayer(l_volume_mu_sigma1, indices=0, axis=-1)
      l_volume_sigma1 = nn.layers.SliceLayer(l_volume_mu_sigma1, indices=1, axis=-1)
  
      l_cdf1 = nn_heart.NormalCDFLayer(nn.layers.reshape(l_volume_mu1, ([0], 1)),
                                       nn.layers.reshape(l_volume_sigma1, ([0], 1)))
      l_outs = [l_cdf0, l_cdf1]
      l_top = nn.layers.MergeLayer(l_outs)
  
      l_target_mu0 = nn.layers.InputLayer((None, 1))
      l_target_mu1 = nn.layers.InputLayer((None, 1))
      l_targets = [l_target_mu0, l_target_mu1]
  
      train_params = nn.layers.get_all_params(l_top)
      test_layes = [l_volume_mu_sigma0, l_volume_mu_sigma1]
  
      return namedtuple('Model', ['l_ins', 'l_outs', 'l_targets', 'l_top', 'train_params', 'submodel', 'test_layers'])(
          l_ins, l_outs,
          l_targets,
          l_top,
          train_params,
          submodel, test_layes)
  
  
  def build_objective(model, deterministic=False):
      p0 = nn.layers.get_output(model.l_outs[0], deterministic=deterministic)
      t0 = nn.layers.get_output(model.l_targets[0])
      t0_heaviside = nn_heart.heaviside(t0)
  
      crps0 = T.mean((p0 - t0_heaviside) ** 2)
  
      p1 = nn.layers.get_output(model.l_outs[1], deterministic=deterministic)
      t1 = nn.layers.get_output(model.l_targets[1])
      t1_heaviside = nn_heart.heaviside(t1)
  
      crps1 = T.mean((p1 - t1_heaviside) ** 2)
  
      return 0.5 * (crps0 + crps1)
  
  
  def build_updates(train_loss, model, learning_rate):
      updates = nn.updates.adam(train_loss, model.train_params, learning_rate)
      return updates
  
  
  def get_mean_validation_loss(batch_predictions, batch_targets):
      return [0, 0]
  
  
  def get_mean_crps_loss(batch_predictions, batch_targets, batch_ids):
      nbatches = len(batch_predictions)
      npredictions = len(batch_predictions[0])
  
      crpss = []
      for i in xrange(npredictions):
          p, t = [], []
          for j in xrange(nbatches):
              p.append(batch_predictions[j][i])
              t.append(batch_targets[j][i])
          p, t = np.vstack(p), np.vstack(t)
          target_cdf = utils_heart.heaviside_function(t)
          crpss.append(np.mean((p - target_cdf) ** 2))
  
      return crpss
  
  
  def get_avg_patient_predictions(batch_predictions, batch_patient_ids, mean):
      return utils_heart.get_patient_average_cdf_predictions(batch_predictions, batch_patient_ids, mean)
  