Getting started

Getting started#

In this chapter, we share with you how to install poli, and get started with calling and registering objective functions.

Installation#

Unfortunately, we only support Linux and MacOS for now.

Installing conda#

poli is built on top of manipulating conda environments. It is therefore important for you to install conda. Follow the documentation of Anaconda itself.

Test that your installation went through by writing

$ conda --version
conda 23.11.0  # Yours might be different, and that's fine.

It’s okay if you have another version. We have tested this documentation on the version showed.

Installing `poli`#

We recommend creating an environment called poli-base.

$ conda create -n poli-base python=3.9
$ conda activate poli-base

Right now, we only support two ways of installing poli: by cloning the repo and installing, or using pip and git+.

Installing using pip git+

If you are not interested in debugging, you can simply run

# in the poli-base env
pip install git+https://github.com/MachineLearningLifeScience/poli.git@dev

dev contains the bleeding-edge version, while master contains a stable release.

Installing for debugging

If you are interested in debugging locally, clone and install as follows:

# in the `poli-base` env.
$ git clone git@github.com:MachineLearningLifeScience/poli.git@dev
$ cd ./poli
$ pip install -e .

A stable version can be found on the master branch, the bleeding-edge is on dev.

Warning

poli works by creating shell scripts inside your home folder, under ./.poli_objectives. Make sure you’re okay with this.

Installing `poli-baselines`#

Warning

poli-baselines is on alpha phase, and the API might change in the short future.

Installing using pip git+

If you are not interested in debugging, you can simply run

# in the poli-base env
pip install git+https://github.com/MachineLearningLifeScience/poli-baselines.git@main

Installing for debugging

If you are interested in debugging locally, clone and install as follows:

# in the `poli-base` env.
$ git clone git@github.com:MachineLearningLifeScience/poli-baselines.git@main
$ cd ./poli-baselines
$ pip install -e .

Testing your installation#

To make sure everything went well, you can test your poli installation by running

$ python -c "from poli.objective_repository import AVAILABLE_BLACK_BOXES ; print(AVAILABLE_BLACK_BOXES.keys())"
dict_keys(['aloha', ..., 'white_noise', 'toy_continuous_problem'])

All problems available will appear, but some of them will have more pre-requisites (e.g. installing foldx or having OpenBabel).

You should also be able to run

$ python -c "import poli_baselines ; print('All good!')"

Your first script using `poli`#

Let’s write a small script that creates an instance of white_noise from the repository:

# see poli/examples/minimal_working_example.py
import numpy as np
from poli import objective_factory

problem = objective_factory.create(name="white_noise")
f, x0 = problem.black_box, problem.x0

x = np.array([["1", "2", "3"]])  # must be of shape [b, L], in this case [1, 3].
for _ in range(5):
    print(f"f(x) = {f(x)}")

If we run this script, it will print 5 evaluations of the objective function on the same input.

white_noise is a trivial example. We include plenty of examples on how to register objective functions that are more complex, including e.g. computing the Quantitative Estimate of Druglikeness of a small molecule or, if you have the foldx simulator installed, how to compute the stability of a protein given a .pdb file.

Getting started

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