Code Interface

Your submission needs to provide at least three components: imports, train(), and infer().

1. imports: As with any script, if your solution contains dependencies on external packages make sure to import them. The system will automatically install your dependencies. Make sure that you are using only packages that are whitelisted here.

2. train(): In the training phase the users will build the model and train it such that it can perform inferences on the testing data. The model must be saved in the resources directory.

3. infer(): In the inference function the model trained in the previous step will be loaded and used to perform inferences on a data sample matching the characteristic of the training test.

A basic but functional submission is available here:

Function Signature

Since Python does not enforce types, you need to make sure that your code will expect these arguments.

def train(
    X_train: pandas.DataFrame,
    y_train: pandas.DataFrame,
    model_directory_path: str
) -> None

def infer(
    X_test: pandas.DataFrame,
    model_directory_path: str
) -> pandas.DataFrame

Hidden parameters

The system has a lot of hidden parameters that the user can use.

Parameter Name	Description
number_of_features	the number of features of the dataset
model_directory_path	the path to the directory to the directory in wich we will be saving your updated model
id_column_name	the name of the id column
moon_column_name	the name of the moon column
target_column_name	the name of the target column
prediction_column_name	the name of the prediction column
moon	the moon currently being processed
current_moon	same as moon
embargo	data embrago
has_gpu	if the runner has a gpu
has_trained	if the moon will train

They can be used in both the train and the infer function:

def train(
    X_train: pandas.DataFrame,
    y_train: pandas.DataFrame,
    id_column_name: str,
    target_column_name: str,
    has_gpu: bool,
    embargo: int,
) -> None

def infer(
    X_test: pandas.DataFrame,
    id_column_name: str,
    prediction_column_name: str,
) -> pandas.DataFrame