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https://github.com/sigopt/sigopt-sklearn

SigOpt wrappers for scikit-learn methods
https://github.com/sigopt/sigopt-sklearn

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SigOpt wrappers for scikit-learn methods

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# SigOpt + scikit-learn Interfacing

[![Build Status](https://travis-ci.org/sigopt/sigopt-sklearn.svg?branch=master)](https://travis-ci.org/sigopt/sigopt_sklearn)



This package implements useful interfaces and wrappers for using [SigOpt](https://sigopt.com) and [scikit-learn](http://scikit-learn.org/stable/) together



## Getting Started



Install the sigopt_sklearn python modules with `pip install sigopt_sklearn`.



Sign up for an account at [https://sigopt.com](https://sigopt.com). To use the interfaces, you'll need your API token

from the [API tokens page](https://sigopt.com/tokens).



### SigOptSearchCV



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



```python

from sklearn import svm, datasets

from sigopt_sklearn.search import SigOptSearchCV



# find your SigOpt client token here : https://sigopt.com/tokens

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.fit(iris.data, iris.target)



# 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



```python

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

clf.fit(X, y)

```



### XGBoostClassifier



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



```python

import xgboost as xgb

from xgboost.sklearn import XGBClassifier

from sklearn import datasets

from sigopt_sklearn.search import SigOptSearchCV



# find your SigOpt client token here : https://sigopt.com/tokens

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)



clf.fit(iris.data, iris.target)

```



### SigOptEnsembleClassifier



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](http://img.youtube.com/vi/9XZ3ihE7OjM/0.jpg)](http://www.youtube.com/watch?v=9XZ3ihE7OjM "SigOpt scikit-learn Hyperparameter Optimization Tutorial")



```

# Human Activity Recognition Using Smartphone

# https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones

wget https://archive.ics.uci.edu/ml/machine-learning-databases/00240/UCI%20HAR%20Dataset.zip

unzip UCI\ HAR\ Dataset.zip

cd UCI\ HAR\ Dataset

```



```python

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 : https://sigopt.com/tokens

sigopt_clf = SigOptEnsembleClassifier()

sigopt_clf.parallel_fit(X_train, y_train, est_timeout=(40 * 60),

    client_token='')



# 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.



```python

from sklearn import svm, datasets

from sigopt_sklearn.search import SigOptSearchCV



# find your SigOpt client token here : https://sigopt.com/tokens

client_token = ''

dataset = datasets.fetch_20newsgroups_vectorized()

X = dataset.data

y = dataset.target



# 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)



clf.fit(X, y)

```



### Categoricals



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



```python

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,



```python

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)



clf.fit(X, y)

```



General Information

=========

repository: 2016-2023