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