model.py

from cloudcasting.models import AbstractModel

import ocf_tsimagemixer


# We define a new class that inherits from AbstractModel
class TSImageMixer(AbstractModel):
    """TSImageMixer model class"""

    def __init__(self, history_steps: int, example_parameter: int) -> None:
        # All models must include `history_steps` as a parameter. This is the number of previous
        # frames that the model uses to makes its predictions. This should not be more than 25, i.e.
        # 6 hours (inclusive of end points) of 15 minutely data.
        # The history_steps parameter should be specified in `validate_config.yml`, along with
        # any other parameters (replace `example_parameter` with as many other parameters as you need to initialize your model, and also add them to `validate_config.yml` under `model: params`)
        super().__init__(history_steps)

        ###### YOUR CODE HERE ######
        # Here you can add any other parameters that you need to initialize your model
        # You might load your trained ML model or set up an optical flow method here.
        # You can also access any code from src/ocf_tsimagemixer, e.g.
        x = ocf_tsimagemixer.load_model()

        ############################

    def forward(self, X):
        # This is where you will make predictions with your model
        # The input X is a numpy array with shape (batch_size, channels, time, height, width)

        ###### YOUR CODE HERE ######
        ...

    def hyperparameters_dict(self):

        # This function should return a dictionary of hyperparameters for the model
        # This is just for your own reference and will be saved with the model scores to wandb

        ###### YOUR CODE HERE ######
        ...