import numpy as np
from ..._types import FloatArrayLike, FloatNDArray
from ..common import _compute_profits
from ._validation import _validate_input_mp
[docs]
def mpcs_score(
y_true: FloatArrayLike,
y_score: FloatArrayLike,
*,
loan_lost_rate: float = 0.275,
roi: float = 0.2644,
check_input: bool = True,
) -> float:
"""
:func:`~empulse.metrics.mpcs()` but only returning the MPCS score.
MPCS presumes a situation where a company is considering whether to grant a loan to a customer.
Correctly identifying defaulters results in receiving a return on investment (ROI), while incorrectly
identifying non-defaulters as defaulters results in a fraction of the loan amount being lost.
For detailed information, consult the paper [1]_.
.. seealso::
:func:`~empulse.metrics.mpcs` : to also return the fraction of loan applications that
should be accepted to maximize profit.
:func:`~empulse.metrics.empcs_score` : for a stochastic version of this metric.
Parameters
----------
y_true : 1D array-like, shape=(n_samples,)
Binary target values ('acquisition': 1, 'no acquisition': 0).
y_score : 1D array-like, shape=(n_samples,)
Target scores, can either be probability estimates or non-thresholded decision values.
loan_lost_rate : float, default=0.275
The fraction of the loan amount which is lost after default (``loan_lost_rate ≥ 0``).
roi : float, default=0.2644
Return on investment on the loan (``roi ≥ 0``).
check_input : bool, default=True
Perform input validation.
Turning off improves performance, useful when using this metric as a loss function.
Returns
-------
mpcs : float
Maximum Profit measure for Credit Scoring.
Notes
-----
The MP measure for Credit Scoring is defined as [1]_:
.. math:: \\max_t \\lambda \\pi_0 F_0(t) - ROI \\pi_1 F_1(t)
The MP measure for Credit Scoring requires that the default class is encoded as 0, and it is NOT interchangeable.
However, this implementation assumes the standard notation ('default': 1, 'no default': 0).
References
----------
.. [1] Verbraken, T., Bravo, C., Weber, R., & Baesens, B. (2014).
Development and application of consumer credit scoring models using profit-based classification measures.
European Journal of Operational Research, 238(2), 505-513.
Examples
--------
>>> from empulse.metrics import mpcs_score
>>>
>>> y_true = [0, 1, 0, 1, 0, 1, 0, 1]
>>> y_score = [0.1, 0.2, 0.3, 0.4, 0.5, 0.7, 0.8, 0.9]
>>> mpcs_score(y_true, y_score)
0.038349999999999995
Using scorer:
>>> import numpy as np
>>> from sklearn.datasets import make_classification
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.model_selection import cross_val_score, StratifiedKFold
>>> from sklearn.metrics import make_scorer
>>> from empulse.metrics import mpcs_score
>>>
>>> X, y = make_classification(random_state=42)
>>> model = LogisticRegression()
>>> cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
>>> scorer = make_scorer(
... mpcs_score, response_method='predict_proba', roi=0.2, loan_lost_rate=0.25
... )
>>> np.mean(cross_val_score(model, X, y, cv=cv, scoring=scorer))
0.123
"""
return mpcs(y_true, y_score, loan_lost_rate=loan_lost_rate, roi=roi, check_input=check_input)[0]
[docs]
def mpcs(
y_true: FloatArrayLike,
y_score: FloatArrayLike,
*,
loan_lost_rate: float = 0.275,
roi: float = 0.2644,
check_input: bool = True,
) -> tuple[float, float]:
"""
Maximum Profit measure for Credit Scoring.
MPCS presumes a situation where a company is considering whether to grant a loan to a customer.
Correctly identifying defaulters results in receiving a return on investment (ROI), while incorrectly
identifying non-defaulters as defaulters results in a fraction of the loan amount being lost.
For detailed information, consult the paper [1]_.
.. seealso::
:func:`~empulse.metrics.mpcs_score` : to only return the MPCS score.
:func:`~empulse.metrics.empcs` : for a stochastic version of this metric.
Parameters
----------
y_true : 1D array-like, shape=(n_samples,)
Binary target values ('acquisition': 1, 'no acquisition': 0).
y_score : 1D array-like, shape=(n_samples,)
Target scores, can either be probability estimates or non-thresholded decision values.
loan_lost_rate : float, default=0.275
The fraction of the loan amount which is lost after default (``loan_lost_rate ≥ 0``).
roi : float, default=0.2644
Return on investment on the loan (``roi ≥ 0``).
check_input : bool, default=True
Perform input validation.
Turning off improves performance, useful when using this metric as a loss function.
Returns
-------
mpcs : float
Maximum Profit measure for Credit Scoring
threshold : float
Fraction of loan applications that should be accepted to maximize profit
Notes
-----
The MP measure for Credit Scoring is defined as [1]_:
.. math:: \\max_t \\lambda \\pi_0 F_0(t) - ROI \\pi_1 F_1(t)
The MP measure for Credit Scoring requires that the default class is encoded as 0, and it is NOT interchangeable.
However, this implementation assumes the standard notation ('default': 1, 'no default': 0).
References
----------
.. [1] Verbraken, T., Bravo, C., Weber, R., & Baesens, B. (2014).
Development and application of consumer credit scoring models using profit-based classification measures.
European Journal of Operational Research, 238(2), 505-513.
Examples
--------
>>> from empulse.metrics import mpcs
>>>
>>> y_true = [0, 1, 0, 1, 0, 1, 0, 1]
>>> y_score = [0.1, 0.2, 0.3, 0.4, 0.5, 0.7, 0.8, 0.9]
>>> mpcs(y_true, y_score)
(0.038349999999999995, 0.875)
"""
profits, customer_thresholds = _compute_profit_credit_scoring(y_true, y_score, loan_lost_rate, roi, check_input)
max_profit_index = np.argmax(profits)
return profits[max_profit_index], customer_thresholds[max_profit_index]
def _compute_profit_credit_scoring(
y_true: FloatArrayLike,
y_score: FloatArrayLike,
frac_loan_lost: float = 0.275,
roi: float = 0.2644,
check_input: bool = True,
) -> tuple[FloatNDArray, FloatNDArray]:
if check_input:
y_true, y_score = _validate_input_mp(y_true, y_score, frac_loan_lost, roi)
else:
y_true = np.asarray(y_true)
y_score = np.asarray(y_score)
cost_benefits = np.array([frac_loan_lost, -roi])
return _compute_profits(y_true, y_score, cost_benefits)
