dragonfly

Installation

See here for detailed instructions on installing Dragonfly and its dependencies.

Quick Installation: If you have done this kind of thing before, you should be able to install Dragonfly via pip.

$ sudo apt-get install python-dev python3-dev gfortran # On Ubuntu/Debian
$ pip install numpy
$ pip install dragonfly-opt -v

Testing the Installation: You can import Dragonfly in python to test if it was installed properly. If you have installed via source, make sure that you move to a different directory to avoid naming conflicts.

$ python
>>> from dragonfly import minimise_function
>>> # The first argument below is the function, the second is the domain, and the third is the budget.
>>> min_val, min_pt, history = minimise_function(lambda x: x ** 4 - x**2 + 0.1 * x, [[-10, 10]], 10);  
...
>>> min_val, min_pt
(-0.32122746026750953, array([-0.7129672]))

Due to stochasticity in the algorithms, the above values for min_val, min_pt may be different. If you run it for longer (e.g. min_val, min_pt, history = minimise_function(lambda x: x ** 4 - x**2 + 0.1 * x, [[-10, 10]], 100)), you should get more consistent values for the minimum.

If the installation fails or if there are warning messages, see detailed instructions here.

 

---

Quick Start

Dragonfly can be used directly in the command line by calling dragonfly-script.py or be imported in python code via the maximise_function function in the main library or in ask-tell mode. To help get started, we have provided some examples in the examples directory. See our readthedocs getting started pages (command line, Python, Ask-Tell) for examples and use cases.

Command line: Below is an example usage in the command line.

$ cd examples
$ dragonfly-script.py --config synthetic/branin/config.json --options options_files/options_example.txt

In Python code: The main APIs for Dragonfly are defined in dragonfly/apis. For their definitions and arguments, see dragonfly/apis/opt.py and dragonfly/apis/moo.py. You can import the main API in python code via,

from dragonfly import minimise_function, maximise_function
func = lambda x: x ** 4 - x**2 + 0.1 * x
domain = [[-10, 10]]
max_capital = 100
min_val, min_pt, history = minimise_function(func, domain, max_capital)
print(min_val, min_pt)
max_val, max_pt, history = maximise_function(lambda x: -func(x), domain, max_capital)
print(max_val, max_pt)

Here, func is the function to be maximised, domain is the domain over which func is to be optimised, and max_capital is the capital available for optimisation. The domain can be specified via a JSON file or in code. See here, here, here, here, here, here, here, here, here, here, and here for more detailed examples.

In Ask-Tell Mode: Ask-tell mode provides you more control over your experiments where you can supply past results to our API in order to obtain a recommendation. See