hpt
A minimal hyperparameter tuning framework to help you train hundreds of models.
It's essentially a set of helpful wrappers over optuna.
Consult the package documentation here!
Install
Install package from PyPI:
pip install hyperparameter-tuning
Getting started
from hpt.tuner import ObjectiveFunction, OptunaTuner
obj_func = ObjectiveFunction(
X_train, y_train, X_test, y_test,
hyperparameter_space=HYPERPARAM_SPACE_PATH, # path to YAML file
eval_metric="accuracy",
s_train=s_train,
s_val=s_test,
threshold=0.50,
)
tuner = OptunaTuner(
objective_function=obj_func,
direction="maximize", # NOTE: can pass other useful study kwargs here (e.g. storage)
)
# Then just run optimize as you would for an optuna.Study object
tuner.optimize(n_trials=20, n_jobs=4)
# Results are stored in tuner.results
tuner.results
# You can reconstruct the best predictor with:
clf = obj_func.reconstruct_model(obj_func.best_trial)
Defining a hyperparameter space
The hyperparameter space is provided either path to a YAML file, or as a dict
with the same structure.
Example hyperparameter spaces here.
The YAML file must follow this structure:
# One or more top-level algorithms
DT:
# Full classpath of algorithm's constructor
classpath: sklearn.tree.DecisionTreeClassifier
# One or more key-word arguments to be passed to the constructor
kwargs:
# Kwargs may be sampled from a distribution
max_depth:
type: int # either 'int' or 'float'
range: [ 10, 100 ] # minimum and maximum values
log: True # (optionally) whether to use logarithmic scale
# Kwargs may be sampled from a fixed set of categories
criterion:
- 'gini'
- 'entropy'
# Kwargs may be a pre-defined value
min_samples_split: 4
# You may explore multiple algorithms at once
LR:
classpath: sklearn.linear_model.LogisticRegression
kwargs:
# An example of a float hyperparameter
C:
type: float
range: [ 0.01, 1.0 ]
log: True
1 maintainer
Metadata
1 maintainer
Release files for hyperparameter-tuning 0.3.2
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