BASE_PATH: ~/Migration_flows
Data_loading:
  covariates:
  - GDP_cap:
      idx: [i, j, k]
      path: input_covariates/GDP_cap
  - GDP_growth:
      idx: [i, j, k]
      path: input_covariates/GDP_growth
  - Trade:
      idx:
      - [j, k]
      - [k, j]
      path: input_covariates/Trade
  - Population:
      idx: [i, j, k]
      path: input_covariates/Population
  - Life_expectancy:
      idx: [i, j, k]
      path: input_covariates/Life_expectancy
  - Birth_rate:
      idx: [j, k]
      path: input_covariates/Birth_rate
  - Death_rate:
      idx: [j, k]
      path: input_covariates/Death_rate
  - Distance:
      idx:
      - [j, k]
      path: input_covariates/Distance
  - Linguistic_similarity:
      idx:
      - [i, k]
      - [j, k]
      path: input_covariates/Linguistic_similarity
  - Religious_similarity:
      idx:
      - [i, k]
      - [j, k]
      path: input_covariates/Religious_similarity
  - Conflict_deaths:
      idx: [j, k]
      path: input_covariates/Conflict_deaths
  - Refugees:
      idx:
      - [i, j]
      - [i, k]
      path: input_covariates/Refugees
  - Colonial_ties:
      idx:
      - [i, k]
      - [j, k]
      path: input_covariates/Colonial_ties
  - EU:
      idx: [i, j, k]
      path: input_covariates/EU
  data_path: Data/Training_data
  data_rescale: 1000.0
  load_args: {}
  load_from_dir: null
NeuralNet:
  activation_funcs:
    default: tanh
    layer_specific:
      -1:
        args: [-12]
        name: celu
  biases:
    default: [-1, 1]
  latent_space_dim: 100
  learning_rate: 0.002
  nodes_per_layer: {default: 60}
  num_layers: 7
  optimizer: Adam
Training:
  Batch_size: 7
  Confidence_band: {flow: 0.01, net_migration: 0.01, stock: 0.01}
  N_epochs: 100000
  Random_sample_size: 70000
  Rescaling:
    flow: {lmbda: 0.5}
    net_migration: {lmbda: 0.5}
    stock: {lmbda: 0.5}
  clip_grad_norm: 1.0
  flow_test_frac: 0.0
  weight_factors: {flow: 1, net_migration: 1, regulariser: 0.1, stock: 1}
  write_every: 10
device: cuda
dry_run: false
path_note: model_5