hypopt 1.0.4

Grid search hyper-parameter optimization using a validation set (not cross validation)

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.

Installation

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 .

Examples

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())
gs.fit(X_train, y_train, param_grid, X_val, y_val)
print('Test Score for Optimized Parameters:', gs.score(X_test, y_test))

Minimal working examples

• Classification minimal working example

• Regression minimal working example

Other Examples including a working example with MNIST

• A simple tutorial of the hypopt package.

• 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, ):
        pass
    def fit(self, X, y, sample_weight = None):
        pass
    def predict(self, X):
        pass
    def score(self, X, y, sample_weight = None):
        pass

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

PyTorch MNIST CNN Example

• Example (taken from the confidentlearning package) of a Pytorch MNIST CNN wrapped in the above class.