example-config.yml

base_dir: /path/to/data/  # path to dataset on disk

# batch size (meta-batch size for MAML)
batch_size: 256

early_stopping_patience: 50
epochs: 300

# after how many epochs the validation set is evaluated
eval_every: 1

# name of the experiment, will be part of the run directory name
experiment_name: example-experiment

# Defines whether during validation/testing the model is finetuned on the support set
finetune: false
finetune_epochs: 5
finetune_lr: 0.01

# defines whether the input and output values are absolute values, deltas, or both. Only makes sense for timeseries
# tasks
input_output_types:
  input: values
  output: values

# Input variables.
input_vars: [0]

learning_rate: 0.0001

# Meta-dataset to use
meta_dataset: datasetname

# Metrics to use in validation
metric:
- mse

# Loss function
loss_fn: mse

# Defines whether early stopping is based on the mean or median validation metric across all tasks
metric_aggregation: mean

# Model type (lstm, feedforward, ...)
model: feedforward

# Number of parallel workers
num_workers: 4

# Optimizer in training and finetuning
optimizer:
  train: adam
  finetune: sgd

# Defines the number of time steps that are used to train and validate the model.
# For non-timeseries tasks, set this to 1
predict_last_n: 1

# Size of the query set in evaluation
query_size: 100

# directory where experiments are stored
run_dir: /path/to/workingdir

# Defines how often the model is saved
save_every: 1

# Random seed
seed: 1

# Length of the input sequence. Set to 1 for non-timeseries tasks.
seq_length: 1

# Size of the support set in finetuning.
support_size: 10

# Target variables. For each variable, you can specify one or more time steps to be predicted.
# For non-timeseries tasks, use 0.
target_vars:
  varname:
    - 0

# Test datasets
test_datasets:
  - dsname_1
  - dsname_2

# If True, the whole timeseries will be viewed as one sample. Else, each time step is a sample. Set to false for
# non-timeseries tasks
timeseries_is_sample: false

# Train datasets
train_datasets:
  - dsname_3

# Training setup (supervised, maml, ...)
training_setup: supervised

# Validation datasets
val_datasets: 
  - dsname_4
  - dsname_5