confopt 0.0.2

Conformal hyperparameter optimization tool

ConfOpt

ConfOpt is an inferential hyperparameter optimization package designed to speed up model hyperparameter tuning.

The package currently implements Adaptive Conformal Hyperparameter Optimization (ACHO), as detailed in the original paper.

Installation

You can install ConfOpt from PyPI using pip:

pip install confopt

Getting Started

As an example, we'll tune a Random Forest model with data from a regression task.

Start by setting up your training and validation data:

from sklearn.datasets import fetch_california_housing

X, y = fetch_california_housing(return_X_y=True)
split_idx = int(len(X) * 0.5)
X_train, y_train = X[:split_idx, :], y[:split_idx]
X_val, y_val = X[split_idx:, :], y[split_idx:]

Then import the Random Forest model to tune and define a search space for its parameters (must be a dictionary mapping the model's parameter names to possible values of that parameter to search):

from sklearn.ensemble import RandomForestRegressor

parameter_search_space = {
    "n_estimators": [10, 30, 50, 100, 150, 200, 300, 400],
    "min_samples_split": [0.005, 0.01, 0.1, 0.2, 0.3],
    "min_samples_leaf": [0.005, 0.01, 0.1, 0.2, 0.3],
    "max_features": [None, 0.8, 0.9, 1],
}

Now import the ConformalSearcher class and initialize it with:

• The model to tune.
• The raw X and y data.
• The parameter search space.
• An extra variable clarifying whether this is a regression or classification problem.

Hyperparameter tuning can be kicked off with the search method and a specification of how long the tuning should run for (in seconds):

from confopt.tuning import ConformalSearcher

searcher = ConformalSearcher(
    model=RandomForestRegressor(),
    X_train=X_train,
    y_train=y_train,
    X_val=X_val,
    y_val=y_val,
    search_space=parameter_search_space,
    prediction_type="regression",
)

searcher.search(
    runtime_budget=120  # How many seconds to run the search for
)

Once done, you can retrieve the best parameters obtained during tuning using:

searcher.get_best_params()

Or obtain an already initialized model with:

searcher.get_best_model()

More information on specific parameters and overrides not mentioned in this walk-through can be found in the docstrings or in the examples folder of the main repository.