https://github.com/andrefcruz/hpt

Hyperparameter tuning with minimal boilerplate
https://github.com/andrefcruz/hpt

easy-to-use hyperparameter-optimization hyperparameter-tuning machine-learning optuna

Last synced: 14 days ago
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Hyperparameter tuning with minimal boilerplate

• Host: GitHub
• URL: https://github.com/andrefcruz/hpt
• Owner: AndreFCruz
• License: mit
• Created: 2022-11-27T16:42:57.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2023-11-30T15:46:50.000Z (about 1 year ago)
• Last Synced: 2024-09-09T16:04:54.810Z (4 months ago)
• Topics: easy-to-use, hyperparameter-optimization, hyperparameter-tuning, machine-learning, optuna
• Language: Python
• Homepage: https://andrefcruz.github.io/hpt/
• Size: 3.36 MB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# hpt

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![PyPI version](https://badgen.net/pypi/v/hyperparameter-tuning)

![OSI license](https://badgen.net/pypi/license/hyperparameter-tuning)

![Python compatibility](https://badgen.net/pypi/python/hyperparameter-tuning)

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](https://andrefcruz.github.io/hpt/)!

## Install

Install package from [PyPI](https://pypi.org/project/hyperparameter-tuning/):

`

pip install hyperparameter-tuning

`

## Getting started

```py

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](examples/hyperparameter_spaces/).

The YAML file must follow this structure:

```yaml

# 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

```