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Adding a new black box to the repository

Adding a new black box to the repository#

This tutorial covers how black boxes and problems are structured in the repository, and what it takes to add a new one.

If you want to implement your own black box and problem, we recommend copy-pasting an existing folder (e.g. dockstring), and modifying it to suit your needs. We provide a checklist at the end of this tutorial for the things you need to pay attention to.

The structure of a problem#

If you take a look at the source code of poli, you will find a folder called objective_repository. This is where all the objective functions in the repository live. The structure of a generic problem goes as follows

poli/objective_repository
├── my_problem_name
│   ├── __init__.py  # Necessary, so that it gets installed with pip.
│   ├── information.py # A BlackBoxInformation containing a desc. of the black box
│   ├── isolated_function.py  # The logic of your black box, as complex as you want.
│   ├── environment.yml  # The conda env where isolated_function.py runs
│   └── register.py  # Boilerplate. the problem factory and black box interface.

You can also have as many other files as you want. Think of the folder .../problem_name as a small project as of itself: you can use any internal code you write here, since it’ll be carried with poli at installation time.

For example: let’s take a look at the problem folder of super_mario_bros

├── super_mario_bros
│   ├── __init__.py
│   ├── environment.yml
│   ├── information.py
│   ├── isolated_function.py
│   ├── register.py
│   ├── requirements.txt
│   ├── example.pt  #    <--
│   ├── level_utils.py # <--
│   ├── model.py  #      <-- Extra files needed
│   ├── simulator.jar  # <-- to run the black box.
│   └── simulator.py  #  <--

Warning

As a general rule: don’t assume that your files will be there after pip install git+.... File endings different from .py and .yml will be ignored by pip during installation. An alternative, then, is to download them in your isolated_function.py or register.py.

Specifying your problem information in information.py#

We recommend you create a script called information.py, and write your black box’s information in it:

# my_problem_name/information.py

from poli.core.black_box_information import BlackBoxInformation

my_problem_information = BlackBoxInformation(
    name="my_problem_name",           # HAS to be the same name as the folder.
    max_sequence_length=2,            # Maximum sequence length (usually np.inf)
    aligned=True,                     # Whether sequences are aligned.
    fixed_length=True,                # Whether sequences have fixed length.
    deterministic=False,              # Whether the problem is deterministic
    alphabet=None,                    # A potential alphabet of accepted tokens
    log_transform_recommended=True,   # Whether the output should be log-transformed
    discrete=False,                   # Discrete inputs
    padding_token="",               # A token that could be used for padding
)

A generic isolated_function.py#

Think of isolated_function.py as the entry-route to all the complex, dependency-heavy logic of your black box.

We expect you to implement a subclass of an AbstractIsolatedFunction. These are dynamically instanced in isolated environments, such as the one you provide in environment.yml.

The average structure of this file would be as follows:

# my_problem_name/isolated_function.py,
# able to run inside the conda env you specify in environment.yml

# Importing whatever you need for the implementation of the black box.
import one_dependency
import another_dependency
from yet_another_dependency import ComplicatedClass

# Importing the abstract isolated function
from poli.core.abstract_isolated_function import AbstractIsolatedFunction

# Your implementation of the isolated logic
# You can have an __init__ if you want!
class MyIsolatedLogic(AbstractIsolatedFunction):
    def __call__(self, x: np.ndarray, context=None) -> np.ndarray:
        """
        Your implementation of the black box function.
        """
        ...

        return y

if __name__ == "__main__":
    from poli.core.registry import register_isolated_function

    register_isolated_function(
        MyIsolatedLogic,  # Your function, designed to be isolated
        name="my_problem_name__isolated",  #  Same name as the problem and folder, ending on __isolated.
        conda_environment_name="conda_env_inside_environment_file",  # The name of the conda env inside environment.yml.
    )

When run, this script will register your isolated logic. By this, we mean creating a shell script inside ~/.poli_objectives that spawns an isolated process with which we communicate when you query new points.

Warning

It is important that name of your isolated function is exactly the name of the folder it’s contained in, followed by __isolated. (We advice using camel_case).

A generic register.py#

The average register.py has the following structure

# my_problem_name/register.py
# This one NEEDS TO run on a conda env. with minimal dependencies (numpy)
from typing import Tuple, List

import numpy as np

from poli.core.abstract_black_box import AbstractBlackBox
from poli.core.abstract_problem_factory import AbstractProblemFactory
from poli.core.black_box_information import BlackBoxInformation
from poli.core.problem import Problem

from poli.core.util.isolation.instancing import instance_function_as_isolated_process

from poli.objective_repository.my_problem_name.information import my_problem_info


class MyBlackBox(AbstractBlackBox):
    def __init__(
        self,
        your_arg: str,
        your_second_arg: List[float],
        your_kwarg: str=...,
        batch_size: int = None,
        parallelize: bool = False,
        num_workers: int = None,
        evaluation_budget: int = float("inf"),
        force_isolation: bool = False,
    ):
        super().__init__(
            batch_size=batch_size,
            parallelize=parallelize,
            num_workers=num_workers,
            evaluation_budget=evaluation_budget,
            force_isolation=force_isolation,
        )

        #... your manipulation of args and kwargs.

        # Importing the isolated logic if we can:
        _ = get_inner_function(
            isolated_function_name="your_problem__isolated",  # <-- modify this
            class_name="MyIsolatedLogic",  # <-- modify this
            module_to_import="poli.objective_repository.your_problem.isolated_function",  # <-- modify this
            force_isolation=force_isolation,
            **other_kwargs_that_go_into_MyIsolatedLogic  # <-- modify this
        )

    # Boilerplate for the black box call:
    def _black_box(self, x: np.ndarray, context: dict = None) -> np.ndarray:
        inner_function = get_inner_function(
            isolated_function_name="your_problem__isolated",  # <-- modify this
            class_name="MyIsolatedLogic",  # <-- modify this
            module_to_import="poli.objective_repository.your_problem.isolated_function",  # <-- modify this
            force_isolation=force_isolation,
            **other_kwargs_that_go_into_MyIsolatedLogic  # <-- modify this
        )
        return inner_function(x, context)

    # A static method that gives you access to the information.
    @staticmethod
    def get_black_box_info() -> BlackBoxInformation:
        return my_problem_info

class MyProblemFactory(AbstractProblemFactory):
    def get_setup_information(self) -> BlackBoxInformation:
        return my_problem_info

    def create(
        self,
        seed: int = None,
        your_arg: str = ...,
        your_second_arg: List[float] = ...,
        your_kwarg: str = ...,
        batch_size: int = None,
        parallelize: bool = False,
        num_workers: int = None,
        evaluation_budget: int = float("inf"),
        your_second_arg: List[float] = ...,
    ) -> Problem:
        # Manipulate args and kwargs you might need at creation time...
        ...
        
        # Creating your black box function
        f = MyBlackBox(
            your_arg=your_arg,
            your_second_arg=your_second_arg,
            your_kwarg=your_kwarg,
            batch_size=batch_size,
            parallelize=parallelize,
            num_workers=num_workers,
            evaluation_budget=evaluation_budget,
        )
        
        # Your first input (an np.array[str] of shape [b, L] or [b,])
        x0 = ...

        return Problem(f, x0)

That is, the script provides an access to your isolated logic. Now users can create a new problem factory or black box without having to worry about having the right dependencies.

Warning

It is important that name of your problem should be the name of the folder it’s contained in, exactly. (We advice using camel_case).

Warning

poli is under active development. The input kwargs to the abstract black box and to the create method are under active development. Your IDE should tell you automatically, though!

A generic environment.yml#

You will usually develop your black-box objective function inside an environment, say your_env. You need to specify all these requirements in the environment.yml, generically:

name: your_env
channels:
  - defaults
dependencies:
  - python=3.9
  - pip
  - pip:
    - numpy
    - "git+https://github.com/MachineLearningLifeScience/poli.git@dev"
    - YOUR OTHER DEPENDENCIES

This environment will be created (if it doesn’t exist yet), and will be used to run isolated_function.py.

Why conda?

Conda environments can be quite good! For example, the super_mario_bros environment contains a Java runtime. This is the environment.yml for said problem:

name: poli__mario
channels:
  - defaults
  - conda-forge
  - pytorch
dependencies:
  - python=3.9
  - conda-forge::openjdk
  - cpuonly
  - pytorch
  - pip
  - pip:
    - numpy
    - click
    - "git+https://github.com/MachineLearningLifeScience/poli.git@dev"

It installs an openjdk that will be added to the path when the environment is active. Moreover, conda is also installable in Google Colab, allowing you to use poli there.

Adding your new black box to the repository’s __init__#

Once you do this, you can add an import to poli/objective_repository/__init__.py:

# Add something like this after other imports
from .my_problem_name.register import MyBlackBox, MyProblemFactory


# Add your problem to the available problem factories and black boxes

AVAILABLE_PROBLEM_FACTORIES = {
    ...,
    "my_problem_name": MyProblemFactory,  # <-- add this
    ...
}

AVAILABLE_BLACK_BOXES = {
    ...,
    "my_problem_name": MyBlackBox,  # <-- add this
    ...
}

Testing your installation#

If you

  1. have put your new problem is inside poli/objective_repository,

  2. have an information.py that describes your black box,

  3. have an isolated_function.py that implements the complex logic of your black box and registers it,

  4. have a register.py that creates your problem factory and black box,

  5. have an environment.yml that describes the environment you use,

  6. have imported your black box and factory in objective_repository/__init__.py,

then you should be set!

To test, you can run

from poli import objective_factory

problem = objective_factory.create(
    name="your_problem",
    ...,
    your_arg_1=...,      # <-- Keywords you (maybe) needed
    your_arg_2=...,       # <-- at your_factory.create(...)
)

or you could just import your black box as

from poli.objective_repository import MyBlackBox

f = MyBlackBox(...)

Submitting a pull request#

If you want to share your problem with us, feel free to create a pull request in our repository following the instructions in our CONTRIBUTING.md: MachineLearningLifeScience/poli

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