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glassbox.orchestrator.base_search

Abstract BaseSearch for grid/randomized search.

BaseSearch 

BaseSearch(estimator, param_space, cv_engine, scoring_func)

Bases: ABC

Abstract base class for search-based model selection.

Parameters:

Name Type Description Default
estimator BaseModel

The model to optimize.

 required
param_space Dict

Parameter search space.

 required
cv_engine BaseSplitter

Cross-validation splitter.

 required
scoring_func Callable

Scoring function used to evaluate candidates.

 required

Attributes:

Name Type Description
best_params_ Dict

Best found parameter set.
best_score_ float

Best scoring value.
best_estimator_ BaseModel

Best estimator instance.
Source code in glassbox/orchestrator/base_search.py 
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def __init__(
    self,
    estimator: BaseModel,
    param_space: Dict,
    cv_engine: BaseSplitter,
    scoring_func: Callable,
) -> None:
    self.estimator: BaseModel = estimator
    self.param_space: Dict = param_space
    self.cv_engine: BaseSplitter = cv_engine
    self.scoring_func: Callable = scoring_func
    self.best_params_: Dict = {}
    self.best_score_: float = 0.0
    self.best_estimator_: BaseModel = estimator

fit 

fit(X, y)

Fit the search object and select the best estimator.

Parameters:

Name Type Description Default
X ndarray

Training data of shape (n_samples, n_features).

 required
y ndarray

Target values of shape (n_samples,).

 required

Returns:

Type Description
Self

The fitted search object.
Source code in glassbox/orchestrator/base_search.py 
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def fit(self, X: np.ndarray, y: np.ndarray) -> Self:
    """
    Fit the search object and select the best estimator.

    Parameters
    ----------
    X : np.ndarray
        Training data of shape (n_samples, n_features).
    y : np.ndarray
        Target values of shape (n_samples,).

    Returns
    -------
    Self
        The fitted search object.
    """
    if X.ndim != 2:
        raise ValueError("X must be a 2D array")
    if y.ndim != 1:
        raise ValueError("y must be a 1D array")
    if X.shape[0] != y.shape[0]:
        raise ValueError(
            "X and y must have the same number of samples"
        )

    self.best_score_ = float("-inf")
    self.best_params_ = {}
    self.best_estimator_ = copy.deepcopy(self.estimator)

    for candidate_params in self._generate_candidates():
        candidate_estimator = copy.deepcopy(self.estimator)
        for key, value in candidate_params.items():
            setattr(candidate_estimator, key, value)

        fold_scores = []
        for train_idx, val_idx in self.cv_engine.split(X, y):
            clone_estimator = copy.deepcopy(candidate_estimator)
            clone_estimator.fit(X[train_idx], y[train_idx])
            predictions = clone_estimator.predict(X[val_idx])
            fold_score = self.scoring_func(y[val_idx], predictions)
            fold_scores.append(fold_score)

        if len(fold_scores) == 0:
            continue

        mean_score = float(np.mean(fold_scores))
        if mean_score > self.best_score_:
            self.best_score_ = mean_score
            self.best_params_ = candidate_params
            self.best_estimator_ = copy.deepcopy(candidate_estimator)

    return self