Moves the links to the MLLS repository

Author: miguelgondu

docs/protein-optimization/contributing/a_new_problem.md

@@ -1,4 +1,4 @@
-# Contributing a new problem to the repository
+# Adding a new problem to the repository
 
 This tutorial covers how problems are structured in the repository, and what it takes to add a new one.
 
@@ -9,16 +9,18 @@ If you take a look at the source code of `poli`, you will find a folder called `
 ```bash
 poli/objective_repository
 ├── problem_name              # Has the name of the registered problem (exactly)
+│   ├── __init__.py           # Necessary, else it doesn't get included in the pip install.
 │   ├── environment.yml       # The env. where ./register.py and the problem will run
 │   └── register.py           # Definition and registration of the problem
 ```
 
 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 have Python dependencies that will get appended to the path at runtime.
 
-**For example:**, let's take a look at the problem folder of `super_mario_bros`
+**For example:** let's take a look at the problem folder of `super_mario_bros`
 
 ```bash
 ├── super_mario_bros
+│   ├── __init__.py         
 │   ├── environment.yml
 │   ├── register.py
 │   ├── example.pt           # < --
@@ -47,6 +49,11 @@ from poli.core.abstract_black_box import AbstractBlackBox
 from poli.core.abstract_problem_factory import AbstractProblemFactory
 from poli.core.problem_setup_information import ProblemSetupInformation
 
+# Files that are in the same folder as
+# register will get added to the
+# PYTHONPATH at runtime.
+from your_local_dependency import ...
+
 
 class YourBlackBox(AbstractBlackBox):
     def __init__(self, L: int = np.inf):
@@ -75,7 +82,12 @@ class YourProblemFactory(AbstractProblemFactory):
             alphabet=alphabet,
         )
 
-    def create(self, seed: int = 0, keyword_1 = ..., keyword_2 = ...) -> Tuple[AbstractBlackBox, np.ndarray, np.ndarray]:
+    def create(
+        self,
+        seed: int = 0,
+        your_keyword_1: str = ...,
+        your_keyword_2: str = ...,
+    ) -> Tuple[AbstractBlackBox, np.ndarray, np.ndarray]:
         # Manipulate keywords you might need at creation time...
         ...
 
@@ -110,7 +122,7 @@ if __name__ == "__main__":
 That is, **the script creates and registers** your problem factory.
 
 :::{warning}
-It is important that name of your problem should be the name of the folder it's contained, exactly. (We advice using `camel_case`).
+It is important that name of your problem should be the name of the folder it's contained, **exactly**. (We advice using `camel_case`).
 :::
 
 ## A generic `environment.yml`
@@ -126,7 +138,7 @@ dependencies:
   - pip
   - pip:
     - numpy
-    - "git+https://github.com/miguelgondu/poli.git"
+    - "git+https://github.com/MachineLearningLifeScience/poli.git"
     - YOUR OTHER DEPENDENCIES
 ```
 
@@ -150,7 +162,7 @@ dependencies:
   - pip:
     - numpy
     - click
-    - "git+https://github.com/miguelgondu/poli.git"
+    - "git+https://github.com/MachineLearningLifeScience/poli.git"
 
 ```
 
@@ -189,8 +201,10 @@ from poli import objective_factory
 problem_info, f, x0, y0, _ = objective_factory.create(
     name="your_problem",
     ...,
-    keyword_1=...,      # <-- Keywords you (maybe) needed
-    keyword_2=...       # <-- at your_factory.create(...)
+    your_keyword_1=...,      # <-- Keywords you (maybe) needed
+    your_keyword_2=...       # <-- at your_factory.create(...)
+                        # For now, only string kwargs are
+                        # supported. 
 )
 ```
 

docs/protein-optimization/contributing/a_new_solver.md

@@ -1,3 +1,3 @@
-# Contributing a new black box optimization algorithm
+# Adding a new black box optimization algorithm
 
-[TODO: write] For now, check [the chapter on creating solvers](../using_poli/the_basics/defining_a_problem_solver.md).
+[TODO: write] For now, check [the chapter on creating solvers](../using_poli/the_basics/defining_a_problem_solver.md).

docs/protein-optimization/getting_started/getting_started.md

@@ -39,7 +39,7 @@ If you are not interested in debugging, you can simply run
 
 ```bash
 # in the poli-base env
-pip install git+https://github.com/miguelgondu/poli.git
+pip install git+https://github.com/MachineLearningLifeScience/poli.git
 ```
 
 :::
@@ -50,7 +50,7 @@ If you are interested in debugging locally, clone and install as follows:
 
 ```bash
 # in the `poli-base` env.
-$ git clone [email protected]:miguelgondu/poli.git
+$ git clone [email protected]:MachineLearningLifeScience/poli.git
 $ cd ./poli
 $ pip install -e .
 ```
@@ -59,6 +59,12 @@ $ pip install -e .
 
 ::::
 
+:::{warning}
+
+`poli` works by creating shell scripts inside your home folder, under `./.poli_objectives`. Make sure you're okay with this.
+
+:::
+
 ### Testing your installation
 
 To make sure everything went well, you can test your `poli` installation by running
@@ -70,9 +76,9 @@ $ python -c "from poli.core.registry import get_problems ; print(get_problems())
 
 If the installation isn't fresh/the only one in your system, you might actually get some registered problems.
 
-## Running `poli` in Colab
+## Running `poli` on Colab
 
-With a little effort, you can run `poli` in Colab. [Check this example](https://colab.research.google.com/drive/1-IISCebWYfu0QhuCJ11wOag8aKOiPtls).
+With a little effort, you can run `poli` on Colab. [Check this example](https://colab.research.google.com/drive/1-IISCebWYfu0QhuCJ11wOag8aKOiPtls).
 
 
 ## Your first `poli` script
@@ -100,6 +106,12 @@ for _ in range(5):
 
 If we run this script, `poli` will ask us to confirm that we want to register/install `"white_noise"` as an objective function (you can deactivate this confirmation step by passing the flag `force_register=True` to `.create`). Afterwards, it will print 5 evaluations of the objective function on the same input.
 
+:::{warning}
+
+In the registration process, `poli` creates a `conda` environment, and **executes a shell script**. Be wary of objective functions you find in the wild.
+
+:::
+
 ## Conclusion
 
 This tutorial showed you how to install `poli`, and register `white_noise`, which is available inside `poli` itself.

docs/protein-optimization/using_poli/the_basics/registering_an_objective_function.md

@@ -126,7 +126,7 @@ dependencies:
   - pip
   - pip:
     - numpy
-    - "git+https://github.com/miguelgondu/poli.git"
+    - "git+https://github.com/MachineLearningLifeScience/poli.git"
 ```
 
 :::{admonition} Why conda? Why an entire environment?

readme.md

@@ -8,7 +8,7 @@ This repository contains documentation on how protein optimization is currently
 The folder `./docs/protein-optimization` is a [Jupyter Book](https://jupyterbook.org/en/stable/intro.html). To build it, create a new environment (`python>=3.9`) and install the requirements:
 
 ```bash
-pip install jupyter-book biopython pandas git+https://github.com/miguelgondu/poli.git 
+pip install jupyter-book biopython pandas git+https://github.com/MachineLearningLifeScience/poli.git 
 ```
 
 ## Installing poli baselines