Source code for evalml.objectives.lead_scoring

"""Lead scoring objective."""

import math

from evalml.objectives.binary_classification_objective import (
    BinaryClassificationObjective,
)


[docs]class LeadScoring(BinaryClassificationObjective): """Lead scoring. Args: true_positives (int): Reward for a true positive. Defaults to 1. false_positives (int): Cost for a false positive. Should be negative. Defaults to -1. """ name = "Lead Scoring" greater_is_better = True score_needs_proba = False perfect_score = math.inf is_bounded_like_percentage = False # Range (-Inf, Inf) expected_range = [float("-inf"), float("inf")] def __init__(self, true_positives=1, false_positives=-1): self.true_positives = true_positives self.false_positives = false_positives
[docs] def objective_function( self, y_true, y_predicted, y_train=None, X=None, sample_weight=None, ): """Calculate the profit per lead. Args: y_predicted (pd.Series): Predicted labels. y_true (pd.Series): True labels. y_train (pd.Series): Ignored. X (pd.DataFrame): Ignored. sample_weight (pd.DataFrame): Ignored. Returns: float: Profit per lead """ y_true = self._standardize_input_type(y_true) y_predicted = self._standardize_input_type(y_predicted) 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) # penalty if our estimator only predicts 1 output by making the score 0 same_class_penalty = (2 - len(set(y_predicted))) * abs(profit_per_lead) return profit_per_lead - same_class_penalty