__all__ = ['build_FICO_dataset']
import numpy as np
import pandas as pd
from pkg_resources import resource_filename
from sklearn.metrics import auc
CDF_BY_RACE_PATH = resource_filename(__name__,
'transrisk_cdf_by_race_ssa.csv')
PERFORMANCE_BY_RACE_PATH = resource_filename(__name__,
'transrisk_performance_by_race_ssa.csv') # pylint: disable=line-too-long
TOTAL_BY_RACE_PATH = resource_filename(__name__,
'totals.csv')
def _cleanup_frame(frame):
"""Rename and re-order columns."""
frame = frame.rename(columns={'Non- Hispanic white': 'White'})
frame = frame.reindex(['Asian', 'Black', 'Hispanic', 'White'],
axis=1)
return frame
def _read_totals():
"""Read the total number of people of each race."""
frame = _cleanup_frame(pd.read_csv(TOTAL_BY_RACE_PATH, index_col=0))
return {r: frame[r]['SSA'] for r in frame.columns}
def _parse_data():
"""Parse sqf data set."""
cdfs = _cleanup_frame(pd.read_csv(CDF_BY_RACE_PATH, index_col=0))
performance = (100
- _cleanup_frame(pd.read_csv(PERFORMANCE_BY_RACE_PATH,
index_col=0)))
return (cdfs / 100, performance / 100)
def _load_data():
totals = _read_totals()
cdfs_df, performance_df = _parse_data()
return totals, cdfs_df, performance_df
def _get_pdfs(cdfs_df):
cdf_vs = np.concatenate([[np.zeros_like(cdfs_df.values[0])],
cdfs_df.values])
pdf_vs = (cdf_vs[1:] - cdf_vs[:-1])
pdfs_df = pd.DataFrame(pdf_vs,
columns=cdfs_df.columns, index=cdfs_df.index)
return pdfs_df
def _calc_tpr_fpr(pdfs_df, performance_df):
dfs = []
for value in [performance_df, 1 - performance_df]:
proportion_per_score = value * pdfs_df
proportion_over_all_scores = proportion_per_score.sum(axis=0)
cum_prop_per_score = proportion_per_score[::-1].cumsum(axis=0)[::-1]
rate = cum_prop_per_score / proportion_over_all_scores
# by sklean convention, thresholds[0]
# represents no instances being predicted positive
# and is arbitrarily set to max(y_score) + 1
# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html
rate.loc[max(rate.index) + 1] = [0] * len(rate.columns)
dfs.append(rate)
tpr_df, fpr_df = dfs # pylint: disable=unbalanced-tuple-unpacking
return tpr_df, fpr_df
def _build_rocs(fpr_df, tpr_df):
rocs = {}
for group in fpr_df.columns:
fprs = fpr_df[group].values[::-1]
tprs = tpr_df[group].values[::-1]
thresholds = fpr_df.index[::-1]
rocs[group] = (fprs,
tprs,
thresholds)
return rocs
[docs]def build_FICO_dataset():
"""Build the FICO dataset.
Dataset of the credit score of TransUnion (called TransRisk).
The TransRisk score is in turn based on
a proprietary model created by FICO,
hence often referred to as FICO scores.
The data is *aggregated*, i.e., there is no outcome
and prediction information per individual,
but summarized statistics for each FICO score
and race/race/ethnicity group.
+---------------+------------------------------------------------------+
| FICO key | Meaning |
+===============+======================================================+
| `total` | Total number of individuals |
+---------------+------------------------------------------------------+
| `totals` | Number of individuals per group |
+---------------+------------------------------------------------------+
| `cdf` | Cumulative distribution function of score per group |
+---------------+------------------------------------------------------+
| `pdf` | Probability distribution function of score per group |
+---------------+------------------------------------------------------+
| `performance` | Fraction of non-defaulters per score and group |
+---------------+------------------------------------------------------+
| `base_rates` | Base rate of non-defaulters per group |
+---------------+------------------------------------------------------+
| `base_rate` | The overall base rate non-defaulters |
+---------------+------------------------------------------------------+
| `proportions` | Fraction of individuals per group |
+---------------+------------------------------------------------------+
| `fpr` | True Positive Rate by score as threshold per group |
+---------------+------------------------------------------------------+
| `tpr` | False Positive Rate by score as threshold per group |
+---------------+------------------------------------------------------+
| `rocs` | ROC per group |
+---------------+------------------------------------------------------+
| `aucs` | ROC AUC per group |
+---------------+------------------------------------------------------+
:return: Dictionary of various aggregated statics
of the FICO credit score.
:rtype: dict
References:
- Based on code (MIT License) by Moritz Hardt
from https://github.com/fairmlbook/fairmlbook.github.io
- https://fairmlbook.org/demographic.html#case-study-credit-scoring
"""
totals, cdfs_df, performance_df = _load_data()
pdfs_df = _get_pdfs(cdfs_df)
total = sum(totals.values())
proportions = {group: total / sum(totals.values())
for group, total in totals.items()}
base_rates = (pdfs_df * performance_df).sum()
base_rate = (base_rates * pd.Series(proportions)).sum()
tpr_df, fpr_df = _calc_tpr_fpr(pdfs_df, performance_df)
rocs = _build_rocs(fpr_df, tpr_df)
aucs = {group: auc(fpr, tpr) for group, (fpr, tpr, _)
in rocs.items()}
return {'total': total,
'totals': totals,
'cdf': cdfs_df,
'pdf': pdfs_df,
'performance': performance_df,
'base_rates': base_rates,
'base_rate': base_rate,
'proportions': proportions,
'fpr': fpr_df,
'tpr': tpr_df,
'rocs': rocs,
'aucs': aucs}
