Generate most exciting inputs (MEIs).

Documentation

Installation

The easiest way to install the package is via pip:

pip install neuro-mei

If you want to install from source from a version, for example version v0.1.0, you can run the following command:

pip install git+https://github.com/sinzlab/mei.git@v0.1.0

Quickstart

Check out the demo notebooks in the demo folder to get started.

• Quickstart
• DataJoint Integration

Simple usage with a pre-trained model:

from mei.methods import gradient_ascent

# Create an instance of the model
model = ... # your model here

method_config = {
    "initial": {"path": "mei.initial.RandomNormal"},
    "optimizer": {"path": "torch.optim.SGD", "kwargs": {"lr": 0.1}},
    "stopper": {"path": "mei.stoppers.NumIterations", "kwargs": {"num_iterations": 1000}},
    "objectives": [
        {"path": "mei.objectives.EvaluationObjective", "kwargs": {"interval": 10}}
    ],
    "device": "cuda",
}

mei, _, _ = gradient_ascent(model, config=method_config, seed=0, shape=(1, 1, 100, 100))

How to run the tests 🧪

Clone this repository and run the following command from within the cloned repository to run all tests:

docker-compose run pytest

How to contribute 🔥

Pull requests (and issues) are always welcome. This section describes some preconditions that pull requests need to fulfill.

Tests

Please make sure your changes pass the tests. Take a look at the test running section for instructions on how to run them. Adding tests for new code is highly recommended.

Code Style

black

This project uses the black code formatter. You can check whether your changes comply with its style by running the following command:

docker-compose run black

Furthermore you can pass a path to the service to have black fix any errors in the Python modules it finds in the given path.

isort

isort is used to sort Python imports. You can check the order of imports by running the following command:

docker-compose run isort

The imports can be sorted by passing a path to the service.