import pandas as pd from .binary_classification_objective import BinaryClassificationObjective [docs]class LeadScoring(BinaryClassificationObjective): """Lead scoring.""" name = "Lead Scoring" greater_is_better = True score_needs_proba = False [docs] def __init__(self, true_positives=1, false_positives=-1): """Create instance. Arguments: true_positives (int) : reward for a true positive false_positives (int) : cost for a false positive. Should be negative. """ self.true_positives = true_positives self.false_positives = false_positives [docs] def objective_function(self, y_true, y_predicted, X=None): """Calculate the profit per lead. Arguments: y_predicted (pd.Series): predicted labels y_true (pd.Series): true labels X (pd.DataFrame): None, not used. Returns: float: profit per lead """ if not isinstance(y_predicted, pd.Series): y_predicted = pd.Series(y_predicted) if not isinstance(y_true, pd.Series): y_true = pd.Series(y_true) true_positives = (y_true & y_predicted).sum() false_positives = (~y_true & y_predicted).sum() profit = self.true_positives * true_positives profit += self.false_positives * false_positives profit_per_lead = profit / len(y_true) return profit_per_lead