<reponame>orchardbirds/bokbokbok<filename>bokbokbok/eval_metrics/classification/binary_eval_metrics.py
import numpy as np
from sklearn.metrics import f1_score
from bokbokbok.utils import clip_sigmoid


def 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. 

    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, dtrain, alpha=alpha, XGBoost=XGBoost):
        """
        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


def WeightedFocalMetric(alpha=1.0, gamma=2.0, XGBoost=False):
    """
    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, dtrain, alpha=alpha, gamma=gamma, XGBoost=XGBoost):
        """
        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


def F1_Score_Binary(XGBoost=False, *args, **kwargs):
    """
    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, data, XGBoost=XGBoost):
        """
        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

import numpy as np
from sklearn.metrics import f1_score
from bokbokbok.utils import clip_sigmoid


def 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. 

    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, dtrain, alpha=alpha, XGBoost=XGBoost):
        """
        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


def WeightedFocalMetric(alpha=1.0, gamma=2.0, XGBoost=False):
    """
    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, dtrain, alpha=alpha, gamma=gamma, XGBoost=XGBoost):
        """
        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


def F1_Score_Binary(XGBoost=False, *args, **kwargs):
    """
    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, data, XGBoost=XGBoost):
        """
        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
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 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 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 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 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 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