# Running Tune experiments with SigOpt#

In this tutorial we introduce SigOpt, while running a simple Ray Tune experiment. Tune’s Search Algorithms integrate with SigOpt and, as a result, allow you to seamlessly scale up a SigOpt optimization process - without sacrificing performance.

SigOpt is a model development platform with built in hyperparameter optimization algorithms. Their technology is closed source, but is designed for optimizing functions that are nondifferentiable, with many local minima, or even unknown but only testable. Therefore, SigOpt necessarily falls in the domain of “derivative-free optimization” and “black-box optimization”. In this example we minimize a simple objective to briefly demonstrate the usage of SigOpt with Ray Tune via SigOptSearch. It’s useful to keep in mind that despite the emphasis on machine learning experiments, Ray Tune optimizes any implicit or explicit objective. Here we assume sigopt==7.5.0 library is installed and an API key exists. To learn more and to obtain the necessary API key, refer to SigOpt website.

Click below to see all the imports we need for this example. You can also launch directly into a Binder instance to run this notebook yourself. Just click on the rocket symbol at the top of the navigation.

import time
import os

import ray
import numpy as np
from ray import tune
from ray.air import session
from ray.tune.search.sigopt import SigOptSearch

if "SIGOPT_KEY" not in os.environ:
raise ValueError(
"SigOpt API Key not found. Please set the SIGOPT_KEY "
"environment variable."
)


Let’s start by defining a simple evaluation function. We artificially sleep for a bit (0.1 seconds) to simulate a long-running ML experiment. This setup assumes that we’re running multiple steps of an experiment and try to tune two hyperparameters, namely width and height, and activation.

def evaluate(step, width, height, activation):
time.sleep(0.1)
activation_boost = 10 if activation=="relu" else 1
return (0.1 + width * step / 100) ** (-1) + height * 0.1 + activation_boost


Next, our objective function takes a Tune config, evaluates the score of your experiment in a training loop, and uses session.report to report the score back to Tune.

def objective(config):
for step in range(config["steps"]):
score = evaluate(step, config["width"], config["height"], config["activation"])
session.report({"iterations": step, "mean_loss": score})


Next we define a search space. The critical assumption is that the optimal hyperparamters live within this space. Yet, if the space is very large, then those hyperparameters may be difficult to find in a short amount of time.

#search_config = {
#    "steps": 100,
#    "width": tune.uniform(0, 20),
#    "height": tune.uniform(-100, 100),
#    "activation": tune.choice(["relu, tanh"])
#}

space = [
{
"name": "width",
"type": "int",
"bounds": {"min": 0, "max": 20},
},
{
"name": "height",
"type": "int",
"bounds": {"min": -100, "max": 100},
},
{
"name": "activation",
"type": "categorical",
"categorical_values": ["relu","tanh"]
}
]


Now we define the search algorithm built from SigOptSearch, constrained to a maximum of 1 concurrent trials.

algo = SigOptSearch(
space,
name="SigOpt Example Experiment",
max_concurrent=1,
metric="mean_loss",
mode="min",
)


The number of samples is the number of hyperparameter combinations that will be tried out. This Tune run is set to 1000 samples. (you can decrease this if it takes too long on your machine).

num_samples = 1000


Finally, we run the experiment to "min"imize the “mean_loss” of the objective by searching space provided above to algo, num_samples times. This previous sentence is fully characterizes the search problem we aim to solve. With this in mind, notice how efficient it is to execute tuner.fit().

tuner = tune.Tuner(
objective,
tune_config=tune.TuneConfig(
metric="mean_loss",
mode="min",
search_alg=algo,
num_samples=num_samples,
),
param_space={"steps": 100},
)
results = tuner.fit()


Here are the hyperparamters found to minimize the mean loss of the defined objective.

print("Best hyperparameters found were: ", results.get_best_result().config)


## Multi-objective optimization with Sigopt#

We define another simple objective.

np.random.seed(0)
vector1 = np.random.normal(0, 0.1, 100)
vector2 = np.random.normal(0, 0.1, 100)

def evaluate(w1, w2):
total = w1 * vector1 + w2 * vector2

def multi_objective(config):
w1 = config["w1"]
w2 = config["total_weight"] - w1

average, std = evaluate(w1, w2)
session.report({"average": average, "std": std, "sharpe": average / std})
time.sleep(0.1)


We define the space manually for SigOptSearch.

space = [
{
"name": "w1",
"type": "double",
"bounds": {"min": 0, "max": 1},
},
]

algo = SigOptSearch(
space,
name="sigopt_multiobj_exp",
observation_budget=num_samples,
max_concurrent=1,
metric=["average", "std", "sharpe"],
mode=["max", "min", "obs"],
)


Finally, we run the experiment using Ray Tune, which in this case requires very little input since most of the construction has gone inside search_algo.

tuner = tune.Tuner(
multi_objective,
tune_config=tune.TuneConfig(
search_alg=algo,
num_samples=num_samples,
),
param_space={"total_weight": 1},
)
results = tuner.fit()


And here are they hyperparameters found to minimize the the objective on average.

print("Best hyperparameters found were: ", results.get_best_result("average", "min").config)


## Incorporating prior beliefs with Sigopt#

If we have information about beneficial hyperparameters within the search space, then we can incorporate this bias via a prior distribution. Without explicitly incorporating a prior, the default is a uniform distribution of preference over the search space. Below we highlight the hyperparamters we expect to be better with a Gaussian prior distribution.

np.random.seed(0)
vector1 = np.random.normal(0.0, 0.1, 100)
vector2 = np.random.normal(0.0, 0.1, 100)
vector3 = np.random.normal(0.0, 0.1, 100)

def evaluate(w1, w2, w3):
total = w1 * vector1 + w2 * vector2 + w3 * vector3

def multi_objective_two(config):
w1 = config["w1"]
w2 = config["w2"]
total = w1 + w2
if total > 1:
w3 = 0
w1 /= total
w2 /= total
else:
w3 = 1 - total

average, std = evaluate(w1, w2, w3)
session.report({"average": average, "std": std})


Now we begin setting up the SigOpt experiment and algorithm. Incorporating a prior distribution over hyperparameters requires establishing a connection with SigOpt via "SIGOPT_KEY" environment variable. Here we create a Gaussian prior over w1 and w2, each independently.

samples = num_samples

conn = Connection(client_token=os.environ["SIGOPT_KEY"])

experiment = conn.experiments().create(
name="prior experiment example",
parameters=[
{
"name": "w1",
"bounds": {"max": 1, "min": 0},
"prior": {"mean": 1 / 3, "name": "normal", "scale": 0.2},
"type": "double",
},
{
"name": "w2",
"bounds": {"max": 1, "min": 0},
"prior": {"mean": 1 / 3, "name": "normal", "scale": 0.2},
"type": "double",
},
],
metrics=[
dict(name="std", objective="minimize", strategy="optimize"),
dict(name="average", strategy="store"),
],
observation_budget=samples,
parallel_bandwidth=1,
)

algo = SigOptSearch(
connection=conn,
experiment_id=experiment.id,
name="sigopt_prior_multi_exp",
max_concurrent=1,
metric=["average", "std"],
mode=["obs", "min"],
)


Finally, we run the experiment using Ray Tune, where search_algo establishes the search space.

tuner = tune.Tuner(
objective,
tune_config=tune.TuneConfig(
search_alg=algo,
num_samples=samples,
)
)
results = tuner.fit()


And here are they hyperparameters found to minimize the the objective on average.

print("Best hyperparameters found were: ", results.get_best_result("average", "min").config)