Defines search spaces for scikit-lean estimators
Project description
sksearchspace
Scikit-learn Search Space Configurations with curated search spaces for scikit-learn estimators.
Usage
from sksearchspace import SearchSpace
from sklearn.tree import DecisionTreeClassifier
estimator_space = SearchSpace.for_sklearn_estimator(DecisionTreeClassifier, seed=42)
estimator_space.sample()
# {'criterion': 'entropy','min_samples_leaf': 15, 'min_samples_split': 11}
estimator_space.sample()
# {'criterion': 'entropy', 'min_samples_leaf': 12, 'min_samples_split': 4}
sksearchspace
uses ConfigSpace for sampling. The ConfigSpace
configuration can be accessed through an attribute:
estimator_space.configuration
# Configuration space object:
# Hyperparameters:
# criterion, Type: Categorical, Choices: {gini, entropy}, Default: gini
# min_samples_leaf, Type: UniformInteger, Range: [1, 20], Default: 1
# min_samples_split, Type: UniformInteger, Range: [2, 20], Default: 2
A json file can be loaded as follows:
with open("search_space.json", "r") as f:
estimator_space = SearchSpace(f.read())
License
Copyright (c) 2020 Thomas J. Fan
Distributed under the terms of the MIT license, pytest is free and open source software.
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