Page Comparison

...

config = {
    "model_name": string,
    "model_type": string, # "PyTorch" or "da_rnn"
    "model_params": {
        # All params from the model's __init__ method.
    },
    "dataset_params": {
        "type": string,
        "relevant_cols": list 
        "training_path": list,
        "validation_path": list,
        "test_path": list,
        "batch_size":integer,
        "forecast_history":integer,
        "forecast_length":integer 
    },
    "training_params": {
        "criterion":string,
        "batch_size": int
        "optimizer": string,
        "optim_params": {
            "lr": number,
            "momentum": number 
            },
        "epochs": integer
    },
    "GCS": {
        "run_save_path": string,
        "project_id": string,
        "credential_path":string
    },
    "wandb": {
        "project": string,
        "name": string, # or None (optional)
        "tags": List[string] # or None (optional)
    }
}

...

scaler_cols optional. The columns you want to scale. If left blank and scaler is present this will default to all columns

feature_paramsparam : optional. An optional block used for creating additional features.

datetime_params use this if you want to automatically date_time features to your model base on the timestamp. IMPORTANT REMEMBER to update n_time_series in model_params (+1 for each additional numerical feauture).

hour : value can be numerical or cyclical

day : value can be numerical or cyclical

weekday : value can be numerical or cyclical

month : value can be numerical or cyclical

year : value can be numerical

"feature_param":
{
   "datetime_params":
      {
      "hour": "cyclical"
      "day": "cyclical"
      
      }
}

interpolate required. Either set to False or takes a parameter block. In the case of a parameter block you will need the following parameters:

...

• sweep (either true or false). This must be set to true if you plan to use a Wandb sweep.

• wandb (either true or false). Set to false if you are planning on using a sweep. Only set to true if you are using Wandb without a sweep.w

• sweep_id:

• use_decoder: Whether to use a decoder when performing long range forecasting (n>1 timesteps)

• n_targets optional: The number of targets should be equal to len(targ_col). Required if doing multi-task forecasting.

...