A Python ML package for parallelized hyper-parameter optimization using a validation set (not cross validation).
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A Python machine learning package for grid search hyper-parameter optimization using a validation set (defaults to cross validation when no validation set is available). This package works for Python 2.7+ and Python 3+, for any model (classification and regression), and runs in parallel on all threads on your CPU automatically.

scikit-learn provides a package for grid-search hyper-parameter optimization **using cross-validation** on the training dataset. Unfortunately, cross-validation is impractically slow for large datasets and fails for small datasets due to the lack of data in each class needed to properly train each fold. Instead, we use a constant validation set to optimize hyper-parameters -- the hypopt package makes this fast (distributed on all CPU threads) and easy (one line of code).

hypopt.model_selection.fit_model_with_grid_search supports grid search hyper-parameter optimization when you already have a validation set , eliminating the extra hours of training time required when using cross-validation. However, when no validation set is given, it defaults to using cross-validation on the training set. This allows you to alows use hypopt anytime you need to do hyper-parameter optimization with grid-search, regardless of whether you use a validation set or cross-validation.


Python 2.7, 3.4, 3.5, and 3.6 are supported.

Stable release:

$ pip install hypopt

Developer (unstable) release:

$ pip install git+https://github.com/cgnorthcutt/hypopt.git

To install the codebase (enabling you to make modifications):

$ conda update pip # if you use conda
$ git clone https://github.com/cgnorthcutt/hypopt.git
$ cd hypopt
$ pip install -e .


Basic usage

# Assuming you already have train, test, val sets and a model.
from hypopt import GridSearch
param_grid = [
  {'C': [1, 10, 100], 'kernel': ['linear']},
  {'C': [1, 10, 100], 'gamma': [0.001, 0.0001], 'kernel': ['rbf']},
# Grid-search all parameter combinations using a validation set.
gs = GridSearch(model = SVR(), param_grid = param_grid)
gs.fit(X_train, y_train, X_val, y_val)
print('Test Score for Optimized Parameters:', gs.score(X_test, y_test))

Choosing the scoring metric to optimize

The default metric is the the model.score() function, so in the previous example SVR().score() is optimized, which defaults to accuracy.

It's easy to use a different scoring metric using the scoring parameter in hypopt.GridSearch.fit():

# This will use f1 score as the scoring metric that you optimize.
gs.fit(X_train, y_train, X_val, y_val, scoring='f1')
  • For classification, hypopt supports these string-named metrics: 'accuracy', 'brier_score_loss', 'average_precision', 'f1', 'f1_micro', 'f1_macro', 'f1_weighted', 'neg_log_loss', 'precision', 'recall', or 'roc_auc'.
  • For regression, hypopt supports: "explained_variance", "neg_mean_absolute_error", "neg_mean_squared_error", "neg_mean_squared_log_error", "neg_median_absolute_error", "r2".

You can also create your own metric your_custom_score_func(y_true, y_pred) by wrapping it into an object using sklearn.metrics.make_scorer like:

from sklearn.metrics import make_scorer
scorer = make_scorer(your_custom_scoring_func)
opt.fit(X_train, y_train, X_val, y_val, scoring=scorer)

Minimal working examples

Other Examples including a working example with MNIST

Use hypopt with any model (PyTorch, Tensorflow, caffe2, scikit-learn, etc.)

All of the features of the hypopt package work with any model. Yes, any model. Feel free to use PyTorch, Tensorflow, caffe2, scikit-learn, mxnet, etc. If you use a scikit-learn model, all hypopt methods will work out-of-the-box. It's also easy to use your favorite model from a non-scikit-learn package, just wrap your model into a Python class that inherets the sklearn.base.BaseEstimator. Here's an example for a generic classifier:

from sklearn.base import BaseEstimator
class YourModel(BaseEstimator): # Inherits sklearn base classifier
    def __init__(self, ):
    def fit(self, X, y, sample_weight = None):
    def predict(self, X):
    def score(self, X, y, sample_weight = None):

    # Inherting BaseEstimator gives you these for free!
    # So if you inherit, there's no need to implement these.
    def get_params(self, deep = True):
    def set_params(self, **params):

PyTorch MNIST CNN Example