Getting started in Python Code


Dragonfly can be imported in python code via the maximise_function or minimise_function functions in the main library.

The main APIs for Dragonfly are declared in dragonfly/ and defined in the dragonfly/apis directory. For their definitions and arguments, see dragonfly/apis/ and dragonfly/apis/

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. In simple sequential settings, max_capital is simply the maximum number of evaluations to func, but it can also be used to specify an available time budget and other forms of resource constraints. The maximise_function returns history along with the optimum value and the corresponding optimum point. history.query_points contains the points queried by the algorithm and history.query_vals contains the function values.

The domain can be specified via a JSON file or in code. See here, here, here, here, here, here, here, here, and here for more detailed examples. You can run them via, for example,

$ python examples/synthetic/branin/
$ python examples/tree_reg/


Multiobjective optimisation

Similarly, you can import the multi-objective optimisation APIs in python code via,

from dragonfly import multiobjective_maximise_functions
pareto_max_values, pareto_points, history = multiobjective_maximise_functions(funcs, domain, max_capital)
pareto_min_values, pareto_points, history = multiobjective_minimise_functions(funcs, domain, max_capital)

Here, funcs is a list of functions to be maximised, domain is the domain and max_capital is the capital available for optimisation. pareto_values are the Pareto optimal function values and pareto_points are the corresponding points in domain. See here and here for examples.


Neural Architecture Search

Dragonfly has APIs for defining neural network architectures, defining distances between them, and also implements the NASBOT algorithm, which uses Bayesian optimisation and optimal transport for neural architecture search. You will first need to install the following dependencies. Cython will also require a C++ library already installed in the system.

$ pip install cython POT

Below is an architecture search demo on a synthetic function. See the examples/nas directory for demos of NASBOT in using some large datasets.

$ cd examples
$ --config synthetic/syn_cnn_1/config.json --options options_files/options_example.txt