from .binary_classification_objective import BinaryClassificationObjective
[docs]class FraudCost(BinaryClassificationObjective):
"""Score the percentage of money lost of the total transaction amount process due to fraud.
Arguments:
retry_percentage (float): What percentage of customers that will retry a transaction if it
is declined. Between 0 and 1. Defaults to 0.5.
interchange_fee (float): How much of each successful transaction you pay.
Between 0 and 1. Defaults to 0.02.
fraud_payout_percentage (float): Percentage of fraud you will not be able 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".
"""
name = "Fraud Cost"
greater_is_better = False
score_needs_proba = False
perfect_score = 0.0
is_bounded_like_percentage = True
expected_range = [0, float("inf")]
def __init__(
self,
retry_percentage=0.5,
interchange_fee=0.02,
fraud_payout_percentage=1.0,
amount_col="amount",
):
self.retry_percentage = retry_percentage
self.interchange_fee = interchange_fee
self.fraud_payout_percentage = fraud_payout_percentage
self.amount_col = amount_col
[docs] def objective_function(self, y_true, y_predicted, X, sample_weight=None):
"""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
X (pd.DataFrame): Data with transaction amounts
sample_weight (pd.DataFrame): Ignored.
Returns:
float: Amount lost to fraud per transaction
"""
X = self._standardize_input_type(X)
y_true = self._standardize_input_type(y_true)
y_predicted = self._standardize_input_type(y_predicted)
self.validate_inputs(y_true, y_predicted)
# extract transaction using the amount columns in users data
try:
transaction_amount = X[self.amount_col]
except KeyError:
raise ValueError("`{}` is not a valid column in X.".format(self.amount_col))
# amount paid if transaction is fraud
fraud_cost = transaction_amount * self.fraud_payout_percentage
# money paid 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
# add a penalty if we output naive predictions
all_one_prediction_cost = (2 - len(set(y_predicted))) * fraud_cost.sum()
loss = false_negatives.sum() + false_positives.sum() + all_one_prediction_cost
loss_per_total_processed = loss / transaction_amount.sum()
return loss_per_total_processed