README.md

Model configuration

The folder _params contains two yaml files model.yaml and trainer.yaml that specify the model setttings and training procedure respectively.
Please update the configuration accordingly.

Model specification

Configuaration of the autoencoder

stagate:
  name: intSTAGATE
  params:
      in_features: 3000
      architecture: MLP
      hidden_dims: [512, 30]
      gradient_clipping: 5.0
      
  optimizer:
    name: ADAM
    params:
      lr: 0.0001
      weight_decay: 0.0001
  scheduler:
    name: STEP_LR
    params:
      step_size: 1000
      gamma: 0.9

• in_feature: the dimension of the input features
• hidden_dims: a list indicates the number of neurons of the hidden layers
• gradient_clipping: float that controls the optimization
• optimizer: configuration of the optimizers

Configuaration of the clustering method

clustering:
  r_seed: 2020
  n_clusters: 7
  model_name: EEE

Configuaration of the classifiers

mlp:
  balanced: True
  test_prop: 0.1
  target_class: 3
  optimizer:
    name: ADAM
    params:
      lr: 0.001
      weight_decay: 0.0001
  scheduler:
    name: STEP_LR
    params:
      step_size: 250
      gamma: 0.5

Trainer specification

Trainer for the autoencoder.

stagate_trainer:
  max_epochs: 1000  # Maximum number of epochs to train
  min_epochs: 500  # Min number of epochs to train
  limit_train_batches: 1.0
  limit_val_batches: 1.0
  limit_test_batches: 1.0
  check_val_every_n_epoch: 100
  log_every_n_steps: 2 # How often to add logging rows (does not write to disk)
  precision: 32
  detect_anomaly: True
  auto_select_gpus: True
  enable_model_summary: False
  gpus: 1 # number of gpus to train on (int) or which GPUs to train on (list or str) applied per node
  num_sanity_val_steps: 10
  track_grad_norm: -1 # Otherwise tracks that norm (2 for 2-norm)
  enable_checkpointing: False

• max_epochs and min_epochs controls the number of the trained epochs. We recommend set > 500. We used max_epochs: 1000 in all the experiments.
• Please refer to the documentation of pytorch.lightning for a detailed decription.

Trainer for the classifier.

classifier_trainer:
  max_epochs: 300 # Maximum number of epochs to train
  min_epochs: 300 # Min number of epochs to train
  limit_train_batches: 1.0
  limit_val_batches: 1.0
  limit_test_batches: 1.0
  check_val_every_n_epoch: 100
  log_every_n_steps: 2 # How often to add logging rows (does not write to disk)
  precision: 32
  detect_anomaly: True
  gpus: 1 # number of gpus to train on (int) or which GPUs to train on (list or str) applied per node
  auto_select_gpus: True
  enable_model_summary: False
  num_sanity_val_steps: 10
  track_grad_norm: -1 # Otherwise tracks that norm (2 for 2-norm)
  enable_checkpointing: False