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Command line interfaces

If you have worked with python for some time you are probably familiar with the argparse package, which allows you to directly pass in additional arguments to your script in the terminal

python --arg1 val1 --arg2 val2

argparse is a very simple way of constructing what is called a command line interfaces (CLI). CLI allows you to interact with your application directly in the terminal instead of having change things in your code. It is essentially a text-based user interface (UI) (in contrast to an graphical user interface (GUI) that we know from all our desktop applications).

However, one limitation of argparse is the possibility of easily defining an CLI with subcommands. If we take git as an example, git is the main command but it has multiple subcommands: push, pull, commit etc. that all can take their own arguments. This kind of second CLI with subcommands is somewhat possible to do using only argparse, however it requires a bit of hacks.

You could of course ask the question why we at all would like to have the possibility of defining such CLI. The main argument here is to give users of our code a single entrypoint to interact with our application instead of having multiple scripts. As long as all subcommands are proper documented, then our interface should be simple to interact with (again think git where each subcommand can be given the -h arg to get specific help).

Instead of using argparse we are here going to look at the click package. click extends the functionalities of argparse to allow for easy definition of subcommands and many other things, which we are not going to touch upon in this module. For completeness we should also mention that click is not the only package for doing this, and of other excellent frameworks for creating command line interfaces easily we can mention Typer.

❔ Exercises

Exercise files

  1. Install click

    pip install click
  2. Create a new python file and add the following code:

    import click
    @click.option('--count', default=1, help='Number of greetings.')
    @click.option('--name', prompt='Your name', help='The person to greet.')
    def hello(count, name):
        """Simple program that greets NAME for a total of COUNT times."""
        for x in range(count):
            click.echo(f"Hello {name}!")
    if __name__ == '__main__':

    try running the program in the following ways

    python --count=3
    python --help
  3. Make sure you understand what the click.command() decorator and click.option decorator does. You can find the full API docs here.

  4. As stated above, the power of using a tool like click is due to its ability to define subcommands. In click this is done through the decorator. To the code example from above, add another command:

    @click.option('--count', default=1, help='Number of greetings.')
    @click.option('--name', prompt='Your name', help='The person to greet.')
    def howdy(count, name):
        for x in range(count):
            click.echo(f"Howdy {name}!")

    and by using the decorator make these commands into subcommands such that you would be able to call the script in the following way

    python hello
    python howdy
  5. As an final exercise we provide you with a script that is ready to run as it is, but your job will be do turn it into a script with multiple subcommands, with multiple arguments for each subcommand.

    1. Start by taking a look at the provided code. It is a simple script that runs the K-nearest neighbour classification algorithm on the iris dataset and produces a plot of the decision boundary.

    2. Create a script that has the following subcommands with input arguments

      • Subcommand train: Load data, train model and save. Should take a single argument -o that specifics the filename the trained model should be saved to.
      • Subcommand infer: Load trained model and runs prediction on input data. Should take two arguments: -i that specifies which trained model to load and -d to specify a user defined datapoint to run inference on.
      • Subcommand plot: Load trained model and constructs the decision boundary plot from the code. Should take two arguments: -i that specifies a trained model to load and -o the file to write the generated plot to
      • Subcommand optim: Load data, runs hyperparameter optimization and prints optimal parameters. Should at least take a single argument that in some way adjust the hyperparameter optimization (free to choose how)

      In the end we like the script to be callable in the following ways

      python train -o 'model.ckpt'
      python infer -i 'model.ckpt' -d [[0,1]]
      python plot -i 'model.ckpt' -o 'generated_plot.png'
      python optim