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evhub / bbopt

Black box hyperparameter optimization made easy.
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blackbox-optimization coconut hyperparameter-optimization hyperparameter-tuning python

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BBopt

Join the chat at https://gitter.im/evhub/bbopt DOI

BBopt aims to provide the easiest hyperparameter optimization you'll ever do. Think of BBopt like Keras (back when Theano was still a thing) for black box optimization: one universal interface for working with any black box optimization backend.

BBopt's features include:

Once you've defined your parameters, training a black box optimization model on those parameters is as simple as

bbopt your_file.py

and serving your file with optimized parameters as easy as

import your_file

Questions? Head over to BBopt's Gitter if you have any questions/comments/etc. regarding BBopt.

Installation

To get going with BBopt, simply install it with

pip install bbopt

or, to also install the extra dependencies necessary for running BBopt's examples, run pip install bbopt[examples].

Basic Usage

To use bbopt, just add

# BBopt setup:
from bbopt import BlackBoxOptimizer
bb = BlackBoxOptimizer(file=__file__)
if __name__ == "__main__":
    bb.run()

to the top of your file, then call a random method like

x = bb.uniform("x", 0, 1)

for each of the tunable parameters in your model, and finally add

bb.maximize(y)      or      bb.minimize(y)

to set the value being optimized. Then, run

bbopt <your file here> -n <number of trials> -j <number of processes>

to train your model, and just

import <your module here>

to serve it!

Note: Neither __file__ nor __name__ are available in Jupyter notebooks. In that case, just setup BBopt with:

import os

# BBopt setup:
from bbopt import BlackBoxOptimizer
bb = BlackBoxOptimizer(data_dir=os.getcwd(), data_name="my_project_name")

Examples

Some examples of BBopt in action:

Full API

Command-Line Interface

The bbopt command is extremely simple in terms of what it actually does. For the command bbopt <file> -n <trials> -j <processes>, BBopt simply runs python <file> a number of times equal to <trials>, split across <processes> different processes.

Why does this work? If you're using the basic boilerplate, then running python <file> will trigger the if __name__ == "__main__": clause, which will run a training episode. But when you go to import your file, the if __name__ == "__main__": clause won't get triggered, and you'll just get served the best parameters found so far. Since the command-line interface is so simple, advanced users who want to use the full API instead of the boilerplate need not use the bbopt command at all. If you want more information on the bbopt command, just run bbopt -h.

Black Box Optimization Methods

Constructor

BlackBoxOptimizer(file, *, tag=None, protocol=None)

BlackBoxOptimizer(data_dir, data_name, *, tag=None, protocol=None)

Create a new bb object; this should be done at the beginning of your program as all the other functions are methods of this object.

file is used by BBopt to figure out where to load and save data to, and should usually just be set to __file__. tag allows additional customization of the BBopt data file for when multiple BBopt instances might be desired for the same file. Specifically, BBopt will save data to os.path.splitext(file)[0] + "_" + tag + extension.

Alternatively, data_dir and data_name can be used to specify where to save and load data to. In that case, BBopt will save data to os.path.join(data_dir, data_name + extension) if no tag is passed, or os.path.join(data_dir, data_name + "_" + tag + extension) if a tag is given.

protocol determines how BBopt serializes data. If None (the default), BBopt will use pickle protocol 2, which is the highest version that works on both Python 2 and Python 3 (unless a json file is present, in which case BBopt will use json). To use the newest protocol instead, pass protocol=-1. If protocol="json", BBopt will use json instead of pickle, which is occasionally useful if you want to access your data outside of Python.

run

BlackBoxOptimizer.run(alg="any_fast")

Start optimizing using the given black box optimization algorithm. Use algs to get the valid values for alg.

If this method is never called, or called with alg="serving", BBopt will just serve the best parameters found so far, which is how the basic boilerplate works. Note that, if no saved parameter data is found, and a guess is present, BBopt will use that, which is a good way of distributing your parameter values without including all your saved parameter data.

algs

BlackBoxOptimizer.algs

A dictionary mapping the valid algorithms for use in run to the pair (backend, kwargs) of the backend and arguments to that backend that the algorithm corresponds to.

Supported algorithms are:

Additionally, there are also some algorithms of the form safe_<other_alg> which use mixture to defer to <other_alg> if <other_alg> supports the parameter definition functions you're using, otherwise default to a suitable replacement.

algs also includes the following pseudo-algorithms which defer to run_meta:

Note: The bayes-skopt backend is only available on Python 3.7+ and the pySOT and openai backends are only available on Python 3+.

run_meta

BlackBoxOptimizer.run_meta(algs, meta_alg="boltzmann_gumbel_exploration")

run_meta is a special version of run that uses the meta_alg algorithm to dynamically pick an algorithm from among the given algs. Both algs and meta_alg can use any algorithms in algs.

run_backend

BlackBoxOptimizer.run_backend(backend, *args, **kwargs)

The base function behind run. Instead of specifying an algorithm, run_backend lets you specify the specific backend you want to call and the parameters you want to call it with. Different backends do different things with the remaining arguments:

Note: The bayes-skopt backend is only available on Python 3.7+ and the pySOT and openai backends are only available on Python 3+.

minimize

BlackBoxOptimizer.minimize(value)

Finish optimizing and set the loss for this run to value. To start another run, call run again.

maximize

BlackBoxOptimizer.maximize(value)

Same as minimize but sets the gain instead of the loss.

remember

BlackBoxOptimizer.remember(info)

Update the current run's "memo" field with the given info dictionary. Useful for saving information about a run that shouldn't actually impact optimization but that you would like to have access to later (using get_best_run, for example).

plot_convergence

BlackBoxOptimizer.plot_convergence(ax=None, yscale=None)

Plot the running best gain/loss over the course of all previous trials. If passed, ax should be the matplotlib axis to plot on and yscale should be the scale for the y axis.

Run BBopt's keras example to generate an example plot.

plot_history

BlackBoxOptimizer.plot_history(ax=None, yscale=None)

Plot the gain/loss at each point over the course of all previous trials. If passed, ax should be the matplotlib axis to plot on and yscale should be the scale for the y axis.

Run BBopt's keras example to generate an example plot.

partial_dependence

BlackBoxOptimizer.partial_dependence(i_name, j_name=None, sample_points=None, n_samples=250, n_points=40)

Calls skopt.plots.partial_dependence using previous trial data. The parameters i_name and j_name should be set to names of the parameters you want for the i and j arguments to skopt.plots.partial_dependence.

plot_partial_dependence_1D

BlackBoxOptimizer.plot_partial_dependence_1D(i_name, ax=None, yscale=None, sample_points=None, n_samples=250, n_points=40)

Plot the partial dependence of i_name on the given matplotlib axis ax and with the given y axis scale yscale. See partial_dependence for the meaning of the other parameters.

Run BBopt's keras example to generate an example plot.

plot_evaluations

BlackBoxOptimizer.plot_evaluations(bins=20)

Calls skopt.plots.plot_evaluations using previous trial data.

Run BBopt's keras example to generate an example plot.

plot_objective

BlackBoxOptimizer.plot_objective(levels=10, n_points=40, n_samples=250, size=2, zscale="linear")

Calls skopt.plots.plot_objective using previous trial data.

Run BBopt's keras example to generate an example plot.

plot_regret

BlackBoxOptimizer.plot_regret([ax, [true_minimum, [yscale]]])

Calls skopt.plots.plot_regret using previous trial data.

Run BBopt's keras example to generate an example plot.

get_skopt_result

BlackBoxOptimizer.get_skopt_result()

Gets an OptimizeResult object usable by skopt.plots functions. Allows for arbitrary manipulation of BBopt optimization results in scikit-optimize including any plotting functions not natively supported by BBopt.

get_current_run

BlackBoxOptimizer.get_current_run()

Get information on the current run, including the values of all parameters encountered so far and the loss/gain of the run if specified yet.

get_best_run

BlackBoxOptimizer.get_best_run()

Get information on the best run so far. These are the parameters that will be used if run is not called.

get_data

BlackBoxOptimizer.get_data(print_data=False)

Dump a dictionary containing "params"—the parameters BBopt knows about and what random function and arguments they were initialized with—and "examples"—all the previous data BBopt has collected. If print_data, pretty prints the data in addition to returning it.

data_file

BlackBoxOptimizer.data_file

The path of the file where BBopt is saving data to.

is_serving

BlackBoxOptimizer.is_serving

Whether BBopt is currently using the "serving" algorithm.

tell_examples

BlackBoxOptimizer.tell_examples(examples)

Add the given examples as in get_data to memory, writing the new data to data_file. Must come before run if you want the new data to be included in the model for that run.

backend

BlackBoxOptimizer.backend

The backend object being used by the current BlackBoxOptimizer instance.

run_id

BlackBoxOptimizer.run_id

The id of the current run if started by the BBopt command-line interface.

Parameter Definition Methods

Every BBopt parameter definition method has the form

bb.<random function>(<name>, <args>, **kwargs)

where

Important note: Once you bind a name to a parameter, you cannot change that parameter's options. Thus, if the options defining your parameters can vary from run to run, you must use a different name for each possible combination.

randrange

BlackBoxOptimizer.randrange(name, stop, **kwargs)

BlackBoxOptimizer.randrange(name, start, stop, step=1, **kwargs)

Create a new parameter modeled by random.randrange(start, stop, step).

Backends which support randrange: scikit-optimize, hyperopt, bayes-skopt, pySOT, openai, random.

randint

BlackBoxOptimizer.randint(name, a, b, **kwargs)

Create a new parameter modeled by random.randint(a, b), which is equivalent to random.randrange(a, b-1).

Backends which support randint: scikit-optimize, hyperopt, bayes-skopt, pySOT, openai, random.

getrandbits

BlackBoxOptimizer.getrandbits(name, k, **kwargs)

Create a new parameter modeled by random.getrandbits(k), which is equivalent to random.randrange(0, 2**k).

Backends which support getrandbits: scikit-optimize, hyperopt, bayes-skopt, pySOT, openai, random.

choice

BlackBoxOptimizer.choice(name, seq, **kwargs)

Create a new parameter modeled by random.choice(seq), which chooses an element from seq.

Backends which support choice: scikit-optimize, hyperopt, bayes-skopt, pySOT, random.

randbool

BlackBoxOptimizer.randbool(name, **kwargs)

Create a new boolean parameter, modeled by the equivalent of random.choice([False, True]).

Backends which support randbool: scikit-optimize, hyperopt, bayes-skopt, pySOT, random.

sample

BlackBoxOptimizer.sample(name, population, k, **kwargs)

Create a new parameter modeled by random.sample(population, k), which chooses k elements from population.

By default, the ordering of elements in the result is random. If random ordering is not important and you're happy to have the same ordering as in population, BlackBoxOptimizer.unshuffled_sample is recommended instead.

Backends which support sample: scikit-optimize, hyperopt, bayes-skopt, pySOT, random.

shuffle

BlackBoxOptimizer.shuffle(name, population, **kwargs)

Create a new parameter modeled by random.shuffle(population). A version that returns the shuffled list instead of shuffling it in place is also supported as BlackBoxOptimizer.shuffled.

Backends which support shuffle: scikit-optimize, hyperopt, bayes-skopt, pySOT, random.

random

BlackBoxOptimizer.random(name, **kwargs)

Create a new parameter modeled by random.random(), which is equivalent to random.uniform(0, 1) except that 1 is disallowed.

Backends which support random: scikit-optimize, hyperopt, bayes-skopt, pySOT, openai, random.

uniform

BlackBoxOptimizer.uniform(name, a, b, **kwargs)

Create a new parameter modeled by random.uniform(a, b), which uniformly selects a float in the range [a, b].

Backends which support uniform: scikit-optimize, hyperopt, bayes-skopt, pySOT, openai, random.

loguniform

BlackBoxOptimizer.loguniform(name, min_val, max_val, **kwargs)

Create a new parameter modeled by

math.exp(random.uniform(math.log(min_val), math.log(max_val)))

which logarithmically selects a float between min_val and max_val.

Backends which support loguniform: scikit-optimize, hyperopt, bayes-skopt, pySOT, openai, random.

normalvariate

BlackBoxOptimizer.normalvariate(name, mu, sigma, **kwargs)

Create a new parameter modeled by random.normalvariate(mu, sigma).

A shortcut for the standard normal distribution is also available via BlackBoxOptimizer.stdnormal.

Backends which support normalvariate: hyperopt, openai, random.

lognormvariate

BlackBoxOptimizer.lognormvariate(name, mu, sigma, **kwargs)

Create a new parameter modeled by random.lognormvariate(mu, sigma) such that the natural log is a normal distribution with mean mu and standard deviation sigma.

Backends which support lognormvariate: hyperopt, openai, random.

rand

BlackBoxOptimizer.rand(name, *shape, **kwargs)

Create a new parameter modeled by numpy.random.rand(*shape), which creates a numpy array of the given shape with entries generated uniformly in [0, 1).

Backends which support rand: scikit-optimize, hyperopt, bayes-skopt, pySOT, openai, random.

randn

BlackBoxOptimizer.randn(name, *shape, **kwargs)

Create a new parameter modeled by numpy.random.randn(*shape), which creates a numpy array of the given shape with entries generated according to a standard normal distribution.

Backends which support randn: hyperopt, openai, random.

param

BlackBoxOptimizer.param(name, func, *args, **kwargs)

Create a new parameter modeled by the parameter definition function func with the given arguments. This function is mostly useful if you want to use a custom backend that implements parameter definition functions not included in BBopt by default.

Writing Your Own Backend

BBopt's backend system is built to be extremely extensible, allowing anyone to write and register their own BBopt backends. The basic template for writing a BBopt backend is as follows:

from bbopt.backends.util import StandardBackend

class MyBackend(StandardBackend):
    backend_name = "my-backend"
    implemented_funcs = [
        # list the random functions you support here
        #  (you don't need to include all random functions,
        #  only base random functions, primarily randrange,
        #  choice, uniform, and normalvariate)
        ...,
    ]

    def setup_backend(self, params, **options):
        # initialize your backend; you can use params
        #  to get the args for each param

    def tell_data(self, new_data, new_losses):
        # load new data points into your backend; new_data is
        #  a list of dictionaries containing data and new_losses
        #  is a list of losses for each of those data points

    def get_next_values(self):
        # return the values you want to use for this run as a dict

MyBackend.register()
MyBackend.register_alg("my_alg")

Once you've written a BBopt backend as above, you simply need to import it to trigger the register calls and enable it to be used in BBopt. For some example BBopt backends, see BBopt's default backends (written in Coconut):