import numpy as np from .binary_classification_objective import BinaryClassificationObjective from evalml.model_understanding.graphs import confusion_matrix [docs]class CostBenefitMatrix(BinaryClassificationObjective): """Score using a cost-benefit matrix. Scores quantify the benefits of a given value, so greater numeric scores represents a better score. Costs and scores can be negative, indicating that a value is not beneficial. For example, in the case of monetary profit, a negative cost and/or score represents loss of cash flow.""" name = "Cost Benefit Matrix" greater_is_better = True score_needs_proba = False perfect_score = np.inf [docs] def __init__(self, true_positive, true_negative, false_positive, false_negative): """Create instance of CostBenefitMatrix. Arguments: true_positive (float): Cost associated with true positive predictions true_negative (float): Cost associated with true negative predictions false_positive (float): Cost associated with false positive predictions false_negative (float): Cost associated with false negative predictions """ if None in {true_positive, true_negative, false_positive, false_negative}: raise ValueError("Parameters to CostBenefitMatrix must all be numeric values.") self.true_positive = true_positive self.true_negative = true_negative self.false_positive = false_positive self.false_negative = false_negative [docs] def objective_function(self, y_true, y_predicted, X=None): """Calculates cost-benefit of the using the predicted and true values. Arguments: y_predicted (pd.Series): Predicted labels y_true (pd.Series): True labels X (pd.DataFrame): Ignored. Returns: float: score """ conf_matrix = confusion_matrix(y_true, y_predicted, normalize_method='all') cost_matrix = np.array([[self.true_negative, self.false_positive], [self.false_negative, self.true_positive]]) total_cost = np.multiply(conf_matrix.values, cost_matrix).sum() return total_cost