Source code for ml_grid.model_classes.adaboost_classifier_class

from typing import Optional

import pandas as pd
from sklearn.ensemble import AdaBoostClassifier
from ml_grid.util import param_space
from ml_grid.util.global_params import global_parameters
from skopt.space import Categorical, Real, Integer

print("Imported AdaBoostClassifier class")


[docs]
class adaboost_class:
    """AdaBoostClassifier with support for both Bayesian and non-Bayesian parameter spaces."""

    def __init__(
        self,
        X: Optional[pd.DataFrame] = None,
        y: Optional[pd.Series] = None,
        parameter_space_size: Optional[str] = None,
    ):
        """Initializes the adaboost_class.

        Args:
            X (Optional[pd.DataFrame]): Feature matrix for training.
                Defaults to None.
            y (Optional[pd.Series]): Target vector for training.
                Defaults to None.
            parameter_space_size (Optional[str]): Size of the parameter space for
                optimization. Defaults to None.
        """
        global_params = global_parameters

[docs]
        self.X = X


[docs]
        self.y = y


        # Use the standard AdaBoostClassifier directly

[docs]
        self.algorithm_implementation = AdaBoostClassifier()


[docs]
        self.method_name = "AdaBoostClassifier"


        # Define the parameter vector space

[docs]
        self.parameter_vector_space = param_space.ParamSpace(parameter_space_size)


        if global_params.bayessearch:
            # Define the parameter space for Bayesian optimization
            self.parameter_space = {
                "algorithm": Categorical(["SAMME.R", "SAMME"]),
                "learning_rate": Real(0.01, 1, prior="log-uniform"),
                "n_estimators": Integer(10, 500),
            }

            # Log parameter space for verification
            #print(f"Bayesian Parameter Space: {self.parameter_space}")

        else:
            # Define the parameter space for traditional grid search
            self.parameter_space = {
                "algorithm": ["SAMME.R", "SAMME"],
                "learning_rate": [0.01, 0.1, 0.5, 1.0],
                "n_estimators": [50, 100, 200, 500],
                "random_state": [None],
            }

            # Log parameter space for verification
            #print(f"Traditional Parameter Space: {self.parameter_space}")

        return None