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."""
name = "Fraud Cost"
greater_is_better = False
score_needs_proba = False
perfect_score = 0.0
is_bounded_like_percentage = True
[docs] def __init__(self, retry_percentage=.5, interchange_fee=.02,
fraud_payout_percentage=1.0, amount_col='amount'):
"""Create instance of FraudCost
Arguments:
retry_percentage (float): What percentage of customers that will retry a transaction if it
is declined. Between 0 and 1. Defaults to .5
interchange_fee (float): How much of each successful transaction you can collect.
Between 0 and 1. Defaults to .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"
"""
self.retry_percentage = retry_percentage
self.interchange_fee = interchange_fee
self.fraud_payout_percentage = fraud_payout_percentage
self.amount_col = amount_col
[docs] def decision_function(self, ypred_proba, threshold=0.0, X=None):
"""Determine if a transaction is fraud given predicted probabilities, threshold, and dataframe with transaction amount.
Arguments:
ypred_proba (ww.DataColumn, pd.Series): Predicted probablities
threshold (float): Dollar threshold to determine if transaction is fraud
X (ww.DataTable, pd.DataFrame): Data containing transaction amounts
Returns:
pd.Series: pd.Series of predicted fraud labels using X and threshold
"""
if X is not None:
X = self._standardize_input_type(X)
ypred_proba = self._standardize_input_type(ypred_proba)
transformed_probs = (ypred_proba.values * X[self.amount_col])
return transformed_probs > threshold
[docs] def objective_function(self, y_true, y_predicted, X):
"""Calculate amount lost to fraud per transaction given predictions, true values, and dataframe with transaction amount.
Arguments:
y_predicted (ww.DataColumn, pd.Series): Predicted fraud labels
y_true (ww.DataColumn, pd.Series): True fraud labels
X (ww.DataTable, pd.DataFrame): Data with transaction amounts
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 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