Hyperopt-sklearn is Hyperopt-based model selection among machine learning algorithms in scikit-learn.
See how to use hyperopt-sklearn through examples or older notebooks
More examples can be found in the Example Usage section of the SciPy paper
Komer B., Bergstra J., and Eliasmith C. "Hyperopt-Sklearn: automatic hyperparameter configuration for Scikit-learn" Proc. SciPy 2014. http://conference.scipy.org/proceedings/scipy2014/pdfs/komer.pdf
Installation from a git clone using pip is supported:
git clone git@github.com:hyperopt/hyperopt-sklearn.git
(cd hyperopt-sklearn && pip install -e .)
If you are familiar with sklearn, adding the hyperparameter search with hyperopt-sklearn is only a one line change from the standard pipeline.
from hpsklearn import HyperoptEstimator, svc
from sklearn import svm
# Load Data
# ...
if use_hpsklearn:
estim = HyperoptEstimator(classifier=svc('mySVC'))
else:
estim = svm.SVC()
estim.fit(X_train, y_train)
print(estim.score(X_test, y_test))
# <<show score here>>Each component comes with a default search space.
The search space for each parameter can be changed or set constant by passing in keyword arguments.
In the following example the penalty parameter is held constant during the search, and the loss and alpha parameters have their search space modified from the default.
from hpsklearn import HyperoptEstimator, sgd
from hyperopt import hp
import numpy as np
sgd_penalty = 'l2'
sgd_loss = hp.pchoice(’loss’, [(0.50, ’hinge’), (0.25, ’log’), (0.25, ’huber’)])
sgd_alpha = hp.loguniform(’alpha’, low=np.log(1e-5), high=np.log(1))
estim = HyperoptEstimator(classifier=sgd(’my_sgd’, penalty=sgd_penalty, loss=sgd_loss, alpha=sgd_alpha))
estim.fit(X_train, y_train)Complete example using the Iris dataset:
from hpsklearn import HyperoptEstimator, any_classifier
from sklearn.datasets import load_iris
from hyperopt import tpe
import numpy as np
# Download the data and split into training and test sets
iris = load_iris()
X = iris.data
y = iris.target
test_size = int(0.2 * len(y))
np.random.seed(13)
indices = np.random.permutation(len(X))
X_train = X[indices[:-test_size]]
y_train = y[indices[:-test_size]]
X_test = X[indices[-test_size:]]
y_test = y[indices[-test_size:]]
# Instantiate a HyperoptEstimator with the search space and number of evaluations
estim = HyperoptEstimator(classifier=any_classifier('my_clf'),
preprocessing=any_preprocessing('my_pre'),
algo=tpe.suggest,
max_evals=100,
trial_timeout=120)
# Search the hyperparameter space based on the data
estim.fit(X_train, y_train)
# Show the results
print(estim.score(X_test, y_test))
# 1.0
print( estim.best_model() )
# {'learner': ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',
# max_depth=3, max_features='log2', max_leaf_nodes=None,
# min_impurity_decrease=0.0, min_impurity_split=None,
# min_samples_leaf=1, min_samples_split=2,
# min_weight_fraction_leaf=0.0, n_estimators=13, n_jobs=1,
# oob_score=False, random_state=1, verbose=False,
# warm_start=False), 'preprocs': (), 'ex_preprocs': ()}Here's an example using MNIST and being more specific on the classifier and preprocessing.
from hpsklearn import HyperoptEstimator, extra_trees
from sklearn.datasets import fetch_mldata
from hyperopt import tpe
import numpy as np
# Download the data and split into training and test sets
digits = fetch_mldata('MNIST original')
X = digits.data
y = digits.target
test_size = int(0.2 * len(y))
np.random.seed(13)
indices = np.random.permutation(len(X))
X_train = X[indices[:-test_size]]
y_train = y[indices[:-test_size]]
X_test = X[indices[-test_size:]]
y_test = y[indices[-test_size:]]
# Instantiate a HyperoptEstimator with the search space and number of evaluations
estim = HyperoptEstimator(classifier=extra_trees('my_clf'),
preprocessing=[],
algo=tpe.suggest,
max_evals=10,
trial_timeout=300)
# Search the hyperparameter space based on the data
estim.fit( X_train, y_train )
# Show the results
print(estim.score(X_test, y_test))
# 0.962785714286
print(estim.best_model())
# {'learner': ExtraTreesClassifier(bootstrap=True, class_weight=None, criterion='entropy',
# max_depth=None, max_features=0.959202875857,
# max_leaf_nodes=None, min_impurity_decrease=0.0,
# min_impurity_split=None, min_samples_leaf=1,
# min_samples_split=2, min_weight_fraction_leaf=0.0,
# n_estimators=20, n_jobs=1, oob_score=False, random_state=3,
# verbose=False, warm_start=False), 'preprocs': (), 'ex_preprocs': ()}Not all of the classifiers/regressors/preprocessing from sklearn have been implemented yet. A list of those currently available is shown below. If there is something you would like that is not on the list, feel free to make an issue or a pull request! The source code for implementing these functions is found here
svc
svc_linear
svc_rbf
svc_poly
svc_sigmoid
liblinear_svc
knn
ada_boost
gradient_boosting
random_forest
extra_trees
decision_tree
sgd
xgboost_classification
multinomial_nb
gaussian_nb
passive_aggressive
linear_discriminant_analysis
quadratic_discriminant_analysis
one_vs_rest
one_vs_one
output_code
For a simple generic search space across many classifiers, use any_classifier. If your data is in a sparse matrix format, use any_sparse_classifier.
svr
svr_linear
svr_rbf
svr_poly
svr_sigmoid
knn_regression
ada_boost_regression
gradient_boosting_regression
random_forest_regression
extra_trees_regression
sgd_regression
xgboost_regression
For a simple generic search space across many regressors, use any_regressor. If your data is in a sparse matrix format, use any_sparse_regressor.
pca
one_hot_encoder
standard_scaler
min_max_scaler
normalizer
ts_lagselector
tfidf
rbm
colkmeans
For a simple generic search space across many preprocessing algorithms, use any_preprocessing.
If you are working with raw text data, use any_text_preprocessing.
Currently only TFIDF is used for text, but more may be added in the future.
Note that the preprocessing parameter in HyperoptEstimator is expecting a list, since various preprocessing steps can be chained together.
The generic search space functions any_preprocessing and any_text_preprocessing already return a list, but the others do not so they should be wrapped in a list.
If you do not want to do any preprocessing, pass in an empty list [].