SigOpt + scikit-learn Interfacing

Build Status

This package implements useful interfaces and wrappers for using SigOpt and scikit-learn together

Getting Started

Install the sigopt_sklearn python modules with pip install sigopt_sklearn.

Sign up for an account at To use the interfaces, you’ll need your API token from the API tokens page.


The simplest use case for SigOpt in conjunction with scikit-learn is optimizing estimator hyperparameters using cross validation. A short example that tunes the parameters of an SVM on a small dataset is provided below

from sklearn import svm, datasets
from import SigOptSearchCV

# find your SigOpt client token here :
client_token = '<YOUR_SIGOPT_CLIENT_TOKEN>'

iris = datasets.load_iris()

# define parameter domains
svc_parameters  = {'kernel': ['linear', 'rbf'], 'C': (0.5, 100)}

# define sklearn estimator
svr = svm.SVC()

# define SigOptCV search strategy
clf = SigOptSearchCV(svr, svc_parameters, cv=5,
    client_token=client_token, n_jobs=5, n_iter=20)

# perform CV search for best parameters and fits estimator
# on all data using best found configuration,

# clf.predict() now uses best found estimator
# clf.best_score_ contains CV score for best found estimator
# clf.best_params_ contains best found param configuration

The objective optimized by default is is the default score associated with an estimator. A custom objective can be used by passing the scoring option to the SigOptSearchCV constructor. Shown below is an example that uses the f1_score already implemented in sklearn

from sklearn.metrics import f1_score, make_scorer
f1_scorer = make_scorer(f1_score)

# define SigOptCV search strategy
clf = SigOptSearchCV(svr, svc_parameters, cv=5, scoring=f1_scorer,
    client_token=client_token, n_jobs=5, n_iter=50)

# perform CV search for best parameters, y)


SigOptSearchCV also works with XGBoost’s XGBClassifier wrapper. A hyperparameter search over XGBClassifier models can be done using the same interface

import xgboost as xgb
from xgboost.sklearn import XGBClassifier
from sklearn import datasets
from import SigOptSearchCV

# find your SigOpt client token here :
client_token = '<YOUR_SIGOPT_CLIENT_TOKEN>'
iris = datasets.load_iris()

xgb_params = {
  'learning_rate': (0.01, 0.5),
  'n_estimators': (10, 50),
  'max_depth': (3, 10),
  'min_child_weight': (6, 12),
  'gamma': (0, 0.5),
  'subsample': (0.6, 1.0),
  'colsample_bytree': (0.6, 1.)

xgbc = XGBClassifier()

clf = SigOptSearchCV(xgbc, xgb_params, cv=5,
    client_token=client_token, n_jobs=5, n_iter=70, verbose=1),


This class concurrently trains and tunes several classification models within sklearn to facilitate model selection efforts when investigating new datasets.

You’ll need to install the sigopt_sklearn library with the extra requirements of xgboost for this aspect of the library to work:

pip install sigopt_sklearn[ensemble]

A short example, using an activity recognition dataset is provided below We also have a video tutorial outlining how to run this example here:

SigOpt scikit-learn Tutorial

# Human Activity Recognition Using Smartphone
unzip UCI\ HAR\
cd UCI\ HAR\ Dataset
import numpy as np
import pandas as pd
from sigopt_sklearn.ensemble import SigOptEnsembleClassifier

def load_datafile(filename):
  X = []
  with open(filename, 'r') as f:
    for l in f:
      X.append(np.array([float(v) for v in l.split()]))
  X = np.vstack(X)
  return X

X_train = load_datafile('train/X_train.txt')
y_train = load_datafile('train/y_train.txt').ravel()
X_test = load_datafile('test/X_test.txt')
y_test = load_datafile('test/y_test.txt').ravel()

# fit and tune several classification models concurrently
# find your SigOpt client token here :
sigopt_clf = SigOptEnsembleClassifier()
sigopt_clf.parallel_fit(X_train, y_train, est_timeout=(40 * 60),

# compare model performance on hold out set
ensemble_train_scores = [est.score(X_train,y_train) for est in sigopt_clf.estimator_ensemble]
ensemble_test_scores = [est.score(X_test,y_test) for est in sigopt_clf.estimator_ensemble]
data = sorted(zip([est.__class__.__name__
                        for est in sigopt_clf.estimator_ensemble], ensemble_train_scores, ensemble_test_scores),
                        reverse=True, key=lambda x: (x[2], x[1]))
pd.DataFrame(data, columns=['Classifier ALGO.', 'Train ACC.', 'Test ACC.'])

CV Fold Timeouts

SigOptSearchCV performs evaluations on cv folds in parallel using joblib. Timeouts are now supported in the master branch of joblib and SigOpt can use this timeout information to learn to avoid hyperparameter configurations that are too slow.

from sklearn import svm, datasets
from import SigOptSearchCV

# find your SigOpt client token here :
client_token = '<YOUR_SIGOPT_CLIENT_TOKEN>'
dataset = datasets.fetch_20newsgroups_vectorized()
X =
y =

# define parameter domains
svc_parameters  = {
  'kernel': ['linear', 'rbf'],
  'C': (0.5, 100),
  'max_iter': (10, 200),
  'tol': (1e-2, 1e-6)
svr = svm.SVC()

# SVM fitting can be quite slow, so we set timeout = 180 seconds
# for each fit.  SigOpt will then avoid configurations that are too slow
clf = SigOptSearchCV(svr, svc_parameters, cv=5, opt_timeout=180,
    client_token=client_token, n_jobs=5, n_iter=40), y)


SigOptSearchCV supports categorical parameters specified as list of string as the kernel parameter is in the SVM example:

svc_parameters  = {'kernel': ['linear', 'rbf'], 'C': (0.5, 100)}

SigOpt also supports non-string valued categorical parameters. For example the hidden_layer_sizes parameter in the MLPRegressor example below,

parameters = {
  'activation': ['relu', 'tanh', 'logistic'],
  'solver': ['lbfgs', 'adam'],
  'alpha': (0.0001, 0.01),
  'learning_rate_init': (0.001, 0.1),
  'power_t': (0.001, 1.0),
  'beta_1': (0.8, 0.999),
  'momentum': (0.001, 1.0),
  'beta_2': (0.8, 0.999),
  'epsilon': (0.00000001, 0.0001),
  'hidden_layer_sizes': {
    'shallow': (100,),
    'medium': (10, 10),
    'deep': (10, 10, 10, 10)
nn = MLPRegressor()
clf = SigOptSearchCV(nn, parameters, cv=5, cv_timeout=240,
    client_token=client_token, n_jobs=5, n_iter=40), y)