bokbokbok.eval_metrics.binary_classification

F1_Score_Binary(XGBoost=False, *args, **kwargs)

Implements the f1_score metric from scikit learn

Parameters:

Name	Type	Description	Default
*args	Any	The arguments to be fed into the scikit learn metric.	()
XGBoost	Bool	Set to True if using XGBoost. We assume LightGBM as default use. Note that you should also set maximize=True in the XGBoost train function	False

Source code in bokbokbok/eval_metrics/classification/binary_eval_metrics.py

def F1_Score_Binary(
    XGBoost: bool = False,
    *args: Any, 
    **kwargs: Any,
    ) -> Callable:
    """
    Implements the f1_score metric
    [from scikit learn](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html#sklearn-metrics-f1-score)

    Args:
        *args: The arguments to be fed into the scikit learn metric.
        XGBoost (Bool): Set to True if using XGBoost. We assume LightGBM as default use.
                        Note that you should also set `maximize=True` in the XGBoost train function

    """
    def binary_f1_score(
        yhat: np.ndarray, 
        data: "xgb.DMatrix", 
        XGBoost: bool = XGBoost
        ) -> Union[tuple[str, Any], tuple[str, Any, bool]]: # needs better typing for f1 but I don't care
        """
        F1 Score.

        Args:
            yhat: Predictions
            dtrain: The XGBoost / LightGBM dataset
            XGBoost (Bool): If XGBoost is to be implemented

        Returns:
            Name of the eval metric, Eval score, Bool to maximise function
        """
        y_true = data.get_label()
        yhat = np.round(yhat)
        if XGBoost:
            return "F1", f1_score(y_true, yhat, *args, **kwargs)
        else:
            return "F1", f1_score(y_true, yhat, *args, **kwargs), True

    return binary_f1_score

WeightedCrossEntropyMetric(alpha=0.5, XGBoost=False)

Calculates the Weighted Cross Entropy Metric by applying a weighting factor alpha, allowing one to trade off recall and precision by up- or down-weighting the cost of a positive error relative to a negative error.

A value alpha > 1 decreases the false negative count, hence increasing the recall. Conversely, setting alpha < 1 decreases the false positive count and increases the precision.

Parameters:

Name	Type	Description	Default
alpha	float	The scale to be applied.	0.5
XGBoost	Bool	Set to True if using XGBoost. We assume LightGBM as default use. Note that you should also set maximize=False in the XGBoost train function	False

Source code in bokbokbok/eval_metrics/classification/binary_eval_metrics.py

def WeightedCrossEntropyMetric(
    alpha: float = 0.5, 
    XGBoost: bool = False
    ) -> Callable:
    """
    Calculates the Weighted Cross Entropy Metric by applying a weighting factor alpha, allowing one to
    trade off recall and precision by up- or down-weighting the cost of a positive error relative to a
    negative error.

    A value alpha > 1 decreases the false negative count, hence increasing the recall.
    Conversely, setting alpha < 1 decreases the false positive count and increases the precision. 

    Args:
        alpha (float): The scale to be applied.
        XGBoost (Bool): Set to True if using XGBoost. We assume LightGBM as default use.
                        Note that you should also set `maximize=False` in the XGBoost train function

    """


    def weighted_cross_entropy_metric(
        yhat: np.ndarray, 
        dtrain: "xgb.DMatrix", 
        alpha=alpha, 
        XGBoost=XGBoost
        ) -> Union[tuple[str, float], tuple[str, float, bool]]:
        """
        Weighted Cross Entropy Metric.

        Args:
            yhat: Predictions
            dtrain: The XGBoost / LightGBM dataset
            alpha (float): Scale applied
            XGBoost (Bool): If XGBoost is to be implemented

        Returns:
            Name of the eval metric, Eval score, Bool to minimise function

        """
        y = dtrain.get_label()
        yhat = clip_sigmoid(yhat)
        elements = - alpha * y * np.log(yhat) - (1 - y) * np.log(1 - yhat)
        if XGBoost:
            return f"WCE_alpha{alpha}", (np.sum(elements) / len(y))
        else:
            return f"WCE_alpha{alpha}", (np.sum(elements) / len(y)), False

    return weighted_cross_entropy_metric

WeightedFocalMetric(alpha=1.0, gamma=2.0, XGBoost=False)

Implements alpha-weighted Focal Loss

The more gamma is increased, the more the model is focussed on the hard, misclassified examples.

A value alpha > 1 decreases the false negative count, hence increasing the recall. Conversely, setting alpha < 1 decreases the false positive count and increases the precision.

Parameters:

Name	Type	Description	Default
alpha	float	The scale to be applied.	1.0
gamma	float	The focusing parameter to be applied	2.0
XGBoost	Bool	Set to True if using XGBoost. We assume LightGBM as default use. Note that you should also set maximize=False in the XGBoost train function	False

Source code in bokbokbok/eval_metrics/classification/binary_eval_metrics.py

def WeightedFocalMetric(
    alpha: float = 1.0, 
    gamma: float = 2.0, 
    XGBoost: bool = False
    ) -> Callable:
    """
    Implements [alpha-weighted Focal Loss](https://arxiv.org/pdf/1708.02002.pdf)

    The more gamma is increased, the more the model is focussed on the hard, misclassified examples.

    A value alpha > 1 decreases the false negative count, hence increasing the recall.
    Conversely, setting alpha < 1 decreases the false positive count and increases the precision. 

    Args:
        alpha (float): The scale to be applied.
        gamma (float): The focusing parameter to be applied
        XGBoost (Bool): Set to True if using XGBoost. We assume LightGBM as default use.
                        Note that you should also set `maximize=False` in the XGBoost train function
    """

    def focal_metric(
        yhat: np.ndarray, 
        dtrain: "xgb.DMatrix", 
        alpha: float = alpha, 
        gamma: float = gamma, 
        XGBoost: bool = XGBoost) -> Union[tuple[str, float], tuple[str, float, bool]]:
        """
        Weighted Focal Loss Metric.

        Args:
            yhat: Predictions
            dtrain: The XGBoost / LightGBM dataset
            alpha (float): Scale applied
            gamma (float): Focusing parameter
            XGBoost (Bool): If XGBoost is to be implemented

        Returns:
            Name of the eval metric, Eval score, Bool to minimise function

        """
        y = dtrain.get_label()
        yhat = clip_sigmoid(yhat)

        elements = (- alpha * y * np.log(yhat) * np.power(1 - yhat, gamma) -
                    (1 - y) * np.log(1 - yhat) * np.power(yhat, gamma))

        if XGBoost:
            return f'Focal_alpha{alpha}_gamma{gamma}', (np.sum(elements) / len(y))
        else:
            return f'Focal_alpha{alpha}_gamma{gamma}', (np.sum(elements) / len(y)), False

    return focal_metric