import pandas as pd
from .objective_base import ObjectiveBase
from evalml.problem_types import ProblemTypes
[docs]class FraudCost(ObjectiveBase):
"""Score the percentage of money lost of the total transaction amount process due to fraud"""
name = "Fraud Cost"
problem_types = [ProblemTypes.BINARY]
needs_fitting = True
greater_is_better = False
uses_extra_columns = True
score_needs_proba = False
[docs] def __init__(self, retry_percentage=.5, interchange_fee=.02,
fraud_payout_percentage=1.0, amount_col='amount', verbose=False):
"""Create instance of FraudCost
Arguments:
retry_percentage (float): what percentage of customers will retry a transaction if it
is declined? Between 0 and 1. Defaults to .5
interchange_fee (float): how much of each successful transaction do you collect?
Between 0 and 1. Defaults to .02
fraud_payout_percentage (float): how percentage of fraud will you be unable to collect.
Between 0 and 1. Defaults to 1.0
amount_col (str): name of column in data that contains the amount. defaults to "amount"
"""
self.retry_percentage = retry_percentage
self.interchange_fee = interchange_fee
self.fraud_payout_percentage = fraud_payout_percentage
self.amount_col = amount_col
super().__init__(verbose=verbose)
[docs] def decision_function(self, y_predicted, extra_cols, threshold):
"""Determine if transaction is fraud given predicted probabilities, dataframe with transaction amount, and threshold
Arguments:
y_predicted (pd.Series): predicted labels
extra_cols (pd.DataFrame): extra data needed
threshold (float): dollar threshold to determine if transaction is fraud
Returns:
pd.Series: series of predicted fraud label using extra cols and threshold
"""
if not isinstance(extra_cols, pd.DataFrame):
extra_cols = pd.DataFrame(extra_cols)
if not isinstance(y_predicted, pd.Series):
y_predicted = pd.Series(y_predicted)
transformed_probs = (y_predicted.values * extra_cols[self.amount_col])
return transformed_probs > threshold
[docs] def objective_function(self, y_predicted, y_true, extra_cols):
"""Calculate amount lost to fraud per transaction given predictions, true values, and dataframe with transaction amount
Arguments:
y_predicted (pd.Series): predicted fraud labels
y_true (pd.Series): true fraud labels
extra_cols (pd.DataFrame): extra data needed
Returns:
float: amount lost to fraud per transaction
"""
if not isinstance(extra_cols, pd.DataFrame):
extra_cols = pd.DataFrame(extra_cols)
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)
# extract transaction using the amount columns in users data
transaction_amount = extra_cols[self.amount_col]
# amount paid if transaction is fraud
fraud_cost = transaction_amount * self.fraud_payout_percentage
# money made from interchange fees on transaction
interchange_cost = transaction_amount * (1 - self.retry_percentage) * self.interchange_fee
# calculate cost of missing fraudulent transactions
false_negatives = (y_true & ~y_predicted) * fraud_cost
# calculate money lost from fees
false_positives = (~y_true & y_predicted) * interchange_cost
loss = false_negatives.sum() + false_positives.sum()
loss_per_total_processed = loss / transaction_amount.sum()
return loss_per_total_processed