diff --git a/foundation-frontiers/posts/2026/08/18/chicago.csl b/foundation-frontiers/posts/2026/08/18/chicago.csl new file mode 100644 index 00000000..f4097749 --- /dev/null +++ b/foundation-frontiers/posts/2026/08/18/chicago.csl @@ -0,0 +1,679 @@ + + \ No newline at end of file diff --git a/foundation-frontiers/posts/2026/08/18/hidden-statistics-credit-risk.qmd b/foundation-frontiers/posts/2026/08/18/hidden-statistics-credit-risk.qmd new file mode 100644 index 00000000..f937587c --- /dev/null +++ b/foundation-frontiers/posts/2026/08/18/hidden-statistics-credit-risk.qmd @@ -0,0 +1,184 @@ +--- +title: "The Hidden Statistics Behind Credit Risk" +subtitle: "Three metrics every credit risk data scientist uses are the same information divergence, which means you can finally put error bars on them" +description: | + Three of the most widely used metrics in credit risk turn out to be classical information divergences. Recovering this foundation gives practitioners confidence intervals on IV, statistically grounded checks for PSI drift, and a principled language for fairness. +categories: + - Foundations & Frontiers + - Statistics + - Information Theory + - Credit Risk +author: + - name: Denis Burakov + affiliations: + - Fellow of the Royal Statistical Society + - name: Agus Sudjianto + affiliations: + - SVP of Risk & Technology, H2O.ai + - Executive in Residence, Center for Trustworthy AI Through Model Risk Management, University of North Carolina Charlotte +date: 2026-08-18 +date-format: long +toc: true +bibliography: references.bib +csl: chicago.csl +execute: + eval: false + echo: false + messages: false + error: false + warning: false +page-layout: article +title-block-banner: true +--- + +A credit risk data scientist is ranking features for a new scorecard. One variable has an Information Value (IV) of 0.12; another scores 0.15. A third feature's Population Stability Index (PSI) has just crept above the 0.10 line that the team treats as a warning threshold. Three numbers, three decisions: keep the 0.15 feature over the 0.12 one? Trigger a model review because PSI crossed the threshold? + +In most credit teams these calls are made on the point estimates alone, as if each number were exact. But every one of them was computed from a finite sample, so each carries sampling noise. Is 0.15 *meaningfully* more predictive than 0.12, or could the order flip on next month's data? Did the population really drift, or did a quiet week thin the sample? Without a measure of uncertainty, there is no principled way to answer, and the data scientist is left trusting thresholds that were never derived from any statistical model. + +It turns out the statistical model was there all along. WoE, IV, and PSI are not ad hoc credit-industry inventions; they are specific instances of a classical information divergence (a measure of how far apart two probability distributions are) with roots in Turing's wartime *weight of evidence* [@good1950probability] and Shannon's information theory [@shannon1948mathematical]. Recognising that connection is not just intellectual housekeeping. Because these metrics are functions of sample proportions, they have sampling distributions, and that lets us attach confidence intervals and probabilistic bounds to quantities the industry has long treated as fixed. This article shows how, organised around the three questions our data scientist is really asking. + +## What these three metrics measure + +Before the statistics, a plain-English refresher for readers who don't live in credit scoring: + +- **Weight of Evidence (WoE)** measures, for a single bin of a feature, how differently "good" and "bad" borrowers are distributed: it is large and positive where bads concentrate, negative where goods do. Formally it is a log likelihood ratio, exactly the quantity Turing and Good called the *weight of evidence* [@good1950probability]. +- **Information Value (IV)** rolls those bin-level signals into one number summarising a feature's overall discriminatory power. Practitioners rank candidate features by it and lean on conventional cut-offs (0.02 weak, 0.10 medium, 0.30 strong). +- **Population Stability Index (PSI)** uses the same arithmetic to compare a feature's distribution *today* against a reference period, and is the standard tool for monitoring drift after a model goes live. + +The thing rarely taught alongside these formulas is that all three are the same underlying quantity. + +## One identity: IV = PSI = Jeffreys divergence + +The textbook IV formula, for a feature binned into $K$ categories with good- and bad-borrower proportions $p_{g,j}$ and $p_{b,j}$ [@siddiqi2017intelligent], turns out to be *exactly* the Jeffreys divergence (the symmetrised Kullback--Leibler divergence [@kullback1951information; @jeffreys1961theory]) between the good and bad distributions: + +$$ +\text{IV} = \sum_{j=1}^{K}(p_{b,j} - p_{g,j})\,\ln\frac{p_{b,j}}{p_{g,j}} \;=\; J(P_b \,\|\, P_g) +$$ {#eq-iv} + +PSI is the same expression with a reference and a comparison distribution in place of bads and goods; a "fairness IV" is the same expression across two demographic groups. One formula, three uses. + +::: {.column-margin} +**One formula, three applications.** IV, PSI, and fairness IV are all the Jeffreys divergence $J(P \| Q)$ applied to different partitions of the data. +::: + +This is more than a notational coincidence. Every property of the Jeffreys divergence (non-negativity, symmetry, zero if and only if the two distributions match) transfers automatically to IV and PSI, grounding those decades-old thresholds in a well-understood statistical quantity [@kullback1951information]. More usefully for our data scientist, because the divergence is a function of sample proportions, it inherits a *sampling distribution*: it can be reported with a standard error. The derivation is short but notation-heavy, so we have boxed it below; the rest of the article only needs the result (**IV and PSI come with error bars**) and what that changes for each of the data scientist's three questions. + +::: {.callout-note collapse="true" appearance="simple"} +## Technical note: where the standard errors come from + +The standard error follows from a single observation: Weight of Evidence is a *centred* log-odds ratio, + +$$ +\text{WoE}_j = \underbrace{\ln\frac{n_{j,b}}{n_{j,g}}}_{\text{bin log-odds}} - \underbrace{\ln\frac{n_b}{n_g}}_{\text{population log-odds}} . +$$ {#eq-woe} + +Reading this through Bayes' theorem in odds form gives the familiar updating rule: bin log-odds = population log-odds + WoE, so WoE is the evidence the bin contributes on top of the prior. In Bayesian terms, this is the logarithm of a Bayes factor. Subtracting the constant population term does not change the variance ($\text{Var}(X-c)=\text{Var}(X)$ for any constant $c$), so the standard error of WoE equals that of the bin log-odds ratio: + +$$ +\text{SE}(\text{WoE}_j) = \sqrt{\frac{1}{n_{j,g}} + \frac{1}{n_{j,b}}} . +$$ {#eq-se-woe} + +This is the same $1/\sqrt{n_j\,p_j(1-p_j)}$ that appears as the standard error of a logistic-regression coefficient [@hand1997statistical]. Since IV is a weighted sum of WoE values, the delta method, a standard tool for propagating uncertainty through a function of estimates, applies (assuming approximate independence across bins): + +$$ +\text{SE}(\text{IV}) = \sqrt{\sum_{j=1}^{K}(p_{b,j} - p_{g,j})^2 \cdot \text{SE}(\text{WoE}_j)^2} . +$$ {#eq-se-iv} + +Because IV = PSI = Jeffreys divergence, the identical formula gives a standard error for PSI whenever it is computed from binned counts. Full derivation in [@sudjianto2025information]. +::: + +## Question 1: Is this feature predictive? + +With a standard error in hand, every IV point estimate becomes an interval, $\text{IV} \pm 1.96 \cdot \text{SE}(\text{IV})$. @fig-iv-ci shows this for eight Home Credit features. The error bars do the work the point estimates cannot: features with nearly identical IV can have widely different precision, so a feature scoring 0.15 may not be reliably ahead of one scoring 0.12 once their intervals overlap. Ranking on the point estimate alone can put the more uncertain feature on top by luck of the draw. + +::: {#fig-iv-ci} +![](images/iv_bar_chart.png){fig-alt="Horizontal bar chart showing Information Value for 8 features with human-readable names, with 95% confidence interval error bars. Features are coloured by group: blue for Credit Bureau, pink for Financial/Behaviour, and purple for Categorical/Profile. Vertical dashed lines mark the conventional Weak (0.02), Medium (0.1), and Strong (0.3) IV thresholds."} + +**Information Value with 95% confidence intervals for eight Home Credit features.** Notice that several features have overlapping intervals despite different point estimates: where the bars overlap, ranking one feature above another is not statistically justified, and the order may not survive the next data refresh. Colours indicate feature group: Credit Bureau (blue), Financial/Behaviour (pink), Categorical/Profile (purple). +::: + +The standard error also suggests a quick check for whether a feature carries *any* signal: the ratio $Z = \text{IV}/\text{SE}(\text{IV})$ (a signal-to-noise ratio) flags how far the estimate sits from zero. Treat this as a screening heuristic, not a clean significance test. Binning choices, sparse bins, smoothing, dependence across bins, and the fact that we typically screen many features at once all distort the nominal error rate, so the number should not be read as a publishable p-value. Its value is in catching features whose apparent signal may be nothing more than sampling noise. That is a coarser check than comparing two features against each other, which is the job of the confidence intervals in @fig-iv-ci. + +## Question 2: Has this population drifted? + +Because PSI shares the formula, it shares the standard error, so the same trick distinguishes real drift from sampling noise. @fig-psi shows PSI computed weekly for the number of credit inquiries against a ten-week reference period, with confidence bands. + +::: {#fig-psi} +![](images/psi_over_time.png){fig-alt="Line chart showing weekly PSI values for number of credit inquiries. A spike around April 2020 has a wide confidence band indicating low sample size. From May onward, PSI is persistently elevated with the confidence band above zero, indicating genuine drift. Coloured threshold zones mark stable (green), minor shift (orange), and major shift (red) regions."} + +**Weekly PSI for number of credit inquiries, with 95% confidence bands.** The two movements to compare: the sharp April 2020 spike has a very wide band: it reflects a collapse in origination volume during lockdown (as few as 82 applications in a week), not a genuine shift. The quieter but *sustained* elevation from May onward keeps its band clear of zero, which is the signature of real drift. Without the bands, both look alike; with them, only the second warrants action. +::: + +This is the practical pay-off for monitoring. A threshold crossing on its own is ambiguous: a thin week can push PSI over the line with no real change in behaviour, while a modest but persistent shift with a confidence band sitting clear of zero is the genuine article. The bands let a model-monitoring team triage alerts (chase the sustained signal, wait out the low-volume blip) instead of re-validating on every spike. + +## Question 3: Does this feature behave differently across groups? + +The third question reuses the same divergence a final way. Computed between two demographic groups rather than between goods and bads, the Jeffreys divergence measures how differently a feature is distributed across those groups, a "demographic IV". Plotting each feature's predictive IV against its demographic IV (@fig-pareto) lays out the performance--fairness trade-off in a single view. + +::: {#fig-pareto} +![](images/pareto_predictive_vs_demographic.png){fig-alt="Scatter plot with Predictive IV on the x-axis and Demographic IV on the y-axis. Each point is a feature, coloured by group. Error bars show 95% confidence intervals on both axes. A horizontal dashed orange line marks a reference line at IV = 0.05."} + +**Performance--fairness trade-off for eight features.** Read the plot by quadrant. The horizontal axis is predictive IV (against the default target); the vertical axis is demographic IV (against applicant sex). Features toward the **bottom-right** are the desirable ones: predictive yet evenly distributed across groups. Features toward the **top** carry a large demographic gap regardless of how predictive they are. The dashed line at 0.05 is a reference level for the discussion below, *not* a regulatory standard. Error bars are 95% confidence intervals on both axes; near the line, the bars show the classification can depend on the confidence level chosen. +::: + +The most prominent outlier is `income_type`, with a demographic IV of 0.35. The reason is structural rather than prejudicial: retired pensioners are 29.7% of female applicants but only 15.0% of males, and salaried government employees are 31.3% of women versus 18.6% of men. The feature is picking up a genuine difference in employment composition, but a scorecard that uses it will treat men and women differently in proportion to that gap, which is exactly what the high demographic IV flags. Whether that is acceptable is a governance and legal judgement; the statistic only quantifies the size of the gap. + +That distinction matters for how the number is used. No single demographic-IV value is in itself a compliance bar, and the uncertainty estimate does not turn a fairness question into an automated pass/fail. What it *does* offer is better governance evidence: a feature at $\text{IV}_{\text{fair}} = 0.048 \pm 0.001$ is in a different position from one at $0.048 \pm 0.020$, even though the point estimates match. A model-risk reviewer can weigh that uncertainty rather than treat the point estimate as exact. + +The same machinery answers a sharper version of the question: do the groups differ not just in how a feature is distributed, but in how *predictive* it is? @fig-density computes IV separately for each group on shared bins. For percent of late installments, the feature is more predictive for women (IV = 0.31) than for men (IV = 0.21), a gap that is unlikely to be sampling noise. Tracing the full performance--fairness frontier, by mixed-integer programming, is developed in the paper [@sudjianto2025information]; the contribution here is the statistical one: making the trade-off quantitative rather than binary. + +::: {#fig-density} +![](images/iv_density_by_group.png){fig-alt="Two-panel figure. Left panel shows two normal density curves for the IV of percent late installments, one for female applicants (pink, IV around 0.31) and one for male applicants (teal, IV around 0.21), with vertical dashed lines at the Weak, Medium, and Strong IV thresholds. Right panel shows the normal density of the IV difference (Male minus Female), centred at minus 0.09, with 95% confidence interval dotted lines and a vertical black line at zero."} + +**IV by sex for percent of late installments.** Left: the IV sampling distributions for female and male applicants barely overlap. Right: the distribution of their difference sits clear of zero, so the feature is meaningfully more predictive for one group than the other, a differential that is invisible if IV is reported as a single number per feature. +::: + +## What to do differently tomorrow + +None of this requires new infrastructure, only reporting the uncertainty that was always implicit in the numbers. A short checklist: + +1. **When ranking features by IV, report the confidence interval, not just the point estimate.** A feature at IV = 0.15 ± 0.04 may not be meaningfully ahead of one at 0.12 ± 0.03. +2. **Treat a PSI alert as a hypothesis, not a verdict.** Check whether the confidence band clears zero before you act: a thin origination week can lift the index over a line while the underlying distribution has barely moved. +3. **Bring the error bars to fairness reviews.** A demographic IV is one draw from a distribution: 0.048 ± 0.001 and 0.048 ± 0.020 should not weigh the same in a model-risk discussion, even though their point estimates are identical. +4. **Read the conventional cut-offs (0.02, 0.10, 0.30) as rules of thumb, not thresholds with the force of law.** The statistics size the question; they do not settle it. + +## Code and data + +All results are reproduced in a companion Jupyter notebook at [github.com/deburky/rwds-submission](https://github.com/deburky/rwds-submission). It uses the [Home Credit - Credit Risk Model Stability](https://huggingface.co/datasets/deburky/home-credit-credit-risk-model-stability) dataset [@homecredit2024] (522,596 loan applications, 8 features) and runs directly from HuggingFace. To keep the confidence intervals visible at the scale of the figures, the analysis uses a 10% sample stratified by week (roughly 52,000 applications, with every week retained); on the full dataset the intervals are correspondingly tighter. WoE encoding, IV, and standard errors use the open-source [FastWoe](https://github.com/xRiskLab/fastwoe) library. + +## Conclusion + +Weight of Evidence, Information Value, and the Population Stability Index are not ad hoc metrics. They are specific instances of the Jeffreys divergence, a quantity statisticians have understood since the 1940s. That single recognition is what gives the delta method something to work on, and the delta method is what supplies the standard errors, confidence intervals, and tests behind every figure above. + +The shift it asks of practitioners is small but real: from reporting credit metrics as exact numbers to reporting them as estimates with uncertainty. That is a modest change in workflow, but it moves credit risk practice from point estimates to inference, which is, after all, what statistics is for. + +::: {.article-btn} +[Back to Foundations & Frontiers](https://realworlddatascience.net/ideas/foundations-and-frontiers/) +::: + +::: {.further-info} +::: grid +::: {.g-col-12 .g-col-md-12} +About the authors +: **Denis Burakov** leads data science and data engineering at Renmoney. He has held senior roles at Amazon, N26, KPMG, and Sberbank, spanning retail and corporate lending, regulatory and managerial risk models, and fraud detection. He is based in Berlin, Germany. + +: **Agus Sudjianto** is SVP of Risk & Technology at H2O.ai and Executive in Residence at the Center for Trustworthy AI Through Model Risk Management, University of North Carolina Charlotte. He was formerly Head of Corporate Model Risk at Wells Fargo. He is a co-creator of PiML and MoDeVa. +::: +::: {.g-col-12 .g-col-md-6} +Copyright and licence +: © 2026 Denis Burakov and Agus Sudjianto + + This article is licensed under a Creative Commons Attribution 4.0 (CC BY 4.0) International licence. + +::: + +::: {.g-col-12 .g-col-md-6} +How to cite +: Burakov, Denis, and Agus Sudjianto. 2026. "The Hidden Statistics Behind Credit Risk." Real World Data Science, June 2026. [URL](https://realworlddatascience.net) +::: +::: +::: + +::: {.callout-note appearance="simple"} +**AI disclosure.** Claude (Anthropic) was used to assist with code development for the companion notebook and to help structure drafts of this article. 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Data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "8c2e4ac2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-12T15:32:18.530230Z", + "iopub.status.busy": "2026-06-12T15:32:18.530133Z", + "iopub.status.idle": "2026-06-12T15:32:20.895583Z", + "shell.execute_reply": "2026-06-12T15:32:20.895187Z" + } + }, + "outputs": [], + "source": [ + "from __future__ import annotations\n", + "\n", + "from pathlib import Path\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "from fastwoe import FastWoe\n", + "from matplotlib import pyplot as plt\n", + "from matplotlib.patches import Patch\n", + "from scipy import stats\n", + "from scipy.stats import norm\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import roc_auc_score\n", + "from sklearn.pipeline import make_pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9c80ea53", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-12T15:32:20.897010Z", + "iopub.status.busy": "2026-06-12T15:32:20.896904Z", + "iopub.status.idle": "2026-06-12T15:32:20.900470Z", + "shell.execute_reply": "2026-06-12T15:32:20.900129Z" + } + }, + "outputs": [], + "source": [ + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "IMAGES = Path(\"../images\")\n", + "IMAGES.mkdir(exist_ok=True)\n", + "\n", + "# -------------------------------------------------------------------------------\n", + "# Color palette\n", + "# -------------------------------------------------------------------------------\n", + "C_DR = \"#d6748c\"\n", + "C_PD = \"#7eadba\"\n", + "C_DPD = \"#9bb594\"\n", + "C_CB = \"#7eadba\"\n", + "C_USER = \"#d6748c\"\n", + "C_CAT = \"#939bc9\"\n", + "C_FAIR = \"#da9359\"\n", + "\n", + "# -------------------------------------------------------------------------------\n", + "# LaTeX-native typography (Computer Modern via usetex)\n", + "# -------------------------------------------------------------------------------\n", + "plt.rcParams.update({\n", + " \"text.usetex\": True,\n", + " \"font.family\": \"serif\",\n", + " \"font.serif\": [\"Computer Modern Roman\"],\n", + " \"axes.unicode_minus\": False,\n", + " \"text.latex.preamble\": r\"\\usepackage{amsmath}\",\n", + "})\n", + "\n", + "# -------------------------------------------------------------------------------\n", + "# LaTeX-safe display names for features\n", + "# -------------------------------------------------------------------------------\n", + "PRETTY = {\n", + " \"pct_late_installments\": \"Pct late installments\",\n", + " \"num_credit_inquiries\": \"Num credit inquiries\",\n", + " \"loan_request_type\": \"Loan request type\",\n", + " \"income_type\": \"Income type\",\n", + " \"education_level\": \"Education level\",\n", + " \"primary_language\": \"Primary language\",\n", + " \"marital_status\": \"Marital status\",\n", + " \"family_status\": \"Family status\",\n", + "}\n", + "\n", + "\n", + "def pretty(name):\n", + " \"\"\"Map a feature name to a LaTeX-safe display label.\"\"\"\n", + " return PRETTY.get(name, name.replace(\"_\", \" \").capitalize())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b931f2ac", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-12T15:32:20.901351Z", + "iopub.status.busy": "2026-06-12T15:32:20.901292Z", + "iopub.status.idle": "2026-06-12T15:32:23.284490Z", + "shell.execute_reply": "2026-06-12T15:32:23.284046Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "52,260 rows, default rate 3.27%\n", + "Weeks: 42 (all preserved)\n", + "Train: 41,808 Test: 10,452\n" + ] + } + ], + "source": [ + "# -------------------------------------------------------------------------------\n", + "# Load processed dataset (local or HuggingFace)\n", + "# -------------------------------------------------------------------------------\n", + "HF = \"hf://datasets/deburky/home-credit-credit-risk-model-stability/home_credit_processed.parquet\"\n", + "LOCAL = Path(\"../data/processed/home_credit_processed.parquet\")\n", + "\n", + "# -------------------------------------------------------------------------------\n", + "# Feature definitions and human-readable names\n", + "# -------------------------------------------------------------------------------\n", + "rename_dict = {\n", + " \"target\": \"is_default\",\n", + " \"education_927M\": \"education_level\",\n", + " \"incometype_1044T\": \"income_type\",\n", + " \"familystate_447L\": \"family_status\",\n", + " \"language1_981M\": \"primary_language\",\n", + " \"maritalst_385M\": \"marital_status\",\n", + " \"requesttype_4525192L\": \"loan_request_type\",\n", + " \"pctinstlsallpaidlat10d_839L\": \"pct_late_installments\",\n", + " \"numberofqueries_373L\": \"num_credit_inquiries\",\n", + "}\n", + "\n", + "cols_to_use = [\"target\", \"WEEK_NUM\", \"date_decision\", \"sex_738L\"] + list(rename_dict.keys())\n", + "\n", + "df = pd.read_parquet(LOCAL if LOCAL.exists() else HF, columns=list(set(cols_to_use)))\n", + "df = df.sort_values(\"date_decision\").reset_index(drop=True)\n", + "df = df.rename(columns=rename_dict)\n", + "\n", + "# -------------------------------------------------------------------------------\n", + "# Subsample: 10% stratified within each week (keeps all weeks, ~52K rows)\n", + "# -------------------------------------------------------------------------------\n", + "rng = np.random.default_rng(42)\n", + "sample_idx = (\n", + " df.groupby(\"WEEK_NUM\")\n", + " .apply(lambda g: g.sample(frac=0.10, random_state=rng.integers(1e9)), include_groups=False)\n", + " .index.get_level_values(1)\n", + ")\n", + "df = df.loc[sample_idx].reset_index(drop=True)\n", + "\n", + "print(f\"{df.shape[0]:,} rows, default rate {df['is_default'].mean():.2%}\")\n", + "print(f\"Weeks: {len(df['WEEK_NUM'].unique())} (all preserved)\")\n", + "\n", + "cat_features = [\n", + " \"education_level\",\n", + " \"income_type\",\n", + " \"family_status\",\n", + " \"primary_language\",\n", + " \"marital_status\",\n", + " \"loan_request_type\",\n", + "]\n", + "num_features = [\n", + " \"pct_late_installments\",\n", + " \"num_credit_inquiries\",\n", + "]\n", + "all_features = cat_features + num_features\n", + "\n", + "X = df[all_features].copy()\n", + "y = df[\"is_default\"]\n", + "sex_binary = (df[\"sex_738L\"] == \"M\").astype(int)\n", + "\n", + "X[cat_features] = X[cat_features].fillna(\"NA\").astype(str)\n", + "X[num_features] = X[num_features].astype(float)\n", + "\n", + "# Temporal split (80/20 by date)\n", + "split_idx = int(len(df) * 0.8)\n", + "ix_train = df.iloc[:split_idx].index\n", + "ix_test = df.iloc[split_idx:].index\n", + "print(f\"Train: {len(ix_train):,} Test: {len(ix_test):,}\")" + ] + }, + { + "cell_type": "markdown", + "id": "29f635a3", + "metadata": {}, + "source": [ + "## 2. IV = PSI = Jeffreys divergence\n", + "\n", + "The paper proves that the industry IV formula is exactly the Jeffreys divergence (symmetric KL):\n", + "\n", + "$$\\text{IV} = \\sum_{j}(p_{b,j} - p_{g,j})\\,\\ln\\frac{p_{b,j}}{p_{g,j}} = D_\\text{KL}(P_b \\| P_g) + D_\\text{KL}(P_g \\| P_b)$$\n", + "\n", + "The same formula on different partitions:\n", + "\n", + "| Partition | Measures |\n", + "| ------------------------------- | ------------------------------------- |\n", + "| Good vs. Bad (target) | **Predictive IV** — feature selection |\n", + "| Reference vs. Current (time) | **PSI** — population drift |\n", + "| Group 0 vs. Group 1 (protected) | **Fairness IV** — demographic bias |\n", + "\n", + "We use [FastWoe](https://github.com/xRiskLab/fastwoe) to compute IV with standard errors out of the box.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "136c482c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-12T15:32:23.285596Z", + "iopub.status.busy": "2026-06-12T15:32:23.285538Z", + "iopub.status.idle": "2026-06-12T15:32:23.840789Z", + "shell.execute_reply": "2026-06-12T15:32:23.839133Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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featureiviv_seiv_ci_loweriv_ci_upperiv_significancen_categoriesgini
6pct_late_installments0.22920.01810.19370.2646Significant80.2388
7num_credit_inquiries0.15870.01480.12970.1877Significant90.2039
5loan_request_type0.11960.01190.09620.1430Significant40.1672
1income_type0.10950.01180.08630.1327Significant80.1790
0education_level0.04830.00770.03310.0634Significant60.1069
3primary_language0.02450.00530.01410.0348Significant30.0769
4marital_status0.02290.00530.01250.0334Significant60.0792
2family_status0.01830.00470.00900.0276Significant60.0678
\n", + "
" + ], + "text/plain": [ + " feature iv iv_se iv_ci_lower iv_ci_upper \\\n", + "6 pct_late_installments 0.2292 0.0181 0.1937 0.2646 \n", + "7 num_credit_inquiries 0.1587 0.0148 0.1297 0.1877 \n", + "5 loan_request_type 0.1196 0.0119 0.0962 0.1430 \n", + "1 income_type 0.1095 0.0118 0.0863 0.1327 \n", + "0 education_level 0.0483 0.0077 0.0331 0.0634 \n", + "3 primary_language 0.0245 0.0053 0.0141 0.0348 \n", + "4 marital_status 0.0229 0.0053 0.0125 0.0334 \n", + "2 family_status 0.0183 0.0047 0.0090 0.0276 \n", + "\n", + " iv_significance n_categories gini \n", + "6 Significant 8 0.2388 \n", + "7 Significant 9 0.2039 \n", + "5 Significant 4 0.1672 \n", + "1 Significant 8 0.1790 \n", + "0 Significant 6 0.1069 \n", + "3 Significant 3 0.0769 \n", + "4 Significant 6 0.0792 \n", + "2 Significant 6 0.0678 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train = X.loc[ix_train].copy()\n", + "X_test = X.loc[ix_test].copy()\n", + "y_train = y.loc[ix_train].copy()\n", + "y_test = y.loc[ix_test].copy()\n", + "\n", + "# -------------------------------------------------------------------------------\n", + "# Fit FastWoe encoder (tree-based binning)\n", + "# -------------------------------------------------------------------------------\n", + "woe_encoder = FastWoe(\n", + " binning_method=\"tree\",\n", + " tree_kwargs={\"max_depth\": 3, \"min_samples_leaf\": 50},\n", + ")\n", + "woe_encoder.fit(X_train, y_train)\n", + "\n", + "# -------------------------------------------------------------------------------\n", + "# Fit WOE Logistic Regression\n", + "# -------------------------------------------------------------------------------\n", + "pipeline = make_pipeline(woe_encoder, LogisticRegression(penalty=None, solver=\"newton-cg\"))\n", + "pipeline.fit(X_train, y_train)\n", + "\n", + "# IV analysis with standard errors, CIs, significance, and Gini\n", + "iv_analysis = woe_encoder.get_iv_analysis()\n", + "iv_analysis" + ] + }, + { + "cell_type": "markdown", + "id": "04c61326", + "metadata": {}, + "source": [ + "## 3. Standard errors, CIs, and hypothesis testing\n", + "\n", + "WoE is log odds shifted by a constant (prior log odds). Because $\\text{Var}(X - K) = \\text{Var}(X)$, WoE inherits the SE of log odds unchanged:\n", + "\n", + "$$\\text{SE}(\\text{WoE}_j) = \\sqrt{\\frac{1}{n_{j,g}} + \\frac{1}{n_{j,b}}}$$\n", + "\n", + "Since IV is a weighted sum of WoE, the delta method gives:\n", + "\n", + "$$\\text{SE}(\\text{IV}) = \\sqrt{\\sum_j (p_{b,j}-p_{g,j})^2 \\cdot \\text{SE}(\\text{WoE}_j)^2}$$\n", + "\n", + "This enables:\n", + "\n", + "- **95% CIs** on IV point estimates\n", + "- **Hypothesis test**: $H_0: \\text{IV} = 0$ (no predictive power), $Z = \\text{IV} / \\text{SE}(\\text{IV})$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "bc7df88b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-12T15:32:23.845717Z", + "iopub.status.busy": "2026-06-12T15:32:23.845381Z", + "iopub.status.idle": "2026-06-12T15:32:24.302607Z", + "shell.execute_reply": "2026-06-12T15:32:24.302180Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 390, + "width": 690 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# -------------------------------------------------------------------------------\n", + "# Figure: IV with 95% confidence intervals, colored by feature group\n", + "# -------------------------------------------------------------------------------\n", + "iv_sorted = iv_analysis.sort_values(\"iv\", ascending=True).reset_index(drop=True)\n", + "\n", + "group_colors = {f: C_CAT for f in cat_features} | {\n", + " f: C_USER for f in num_features if f != \"num_credit_inquiries\"\n", + "}\n", + "group_colors[\"num_credit_inquiries\"] = C_CB\n", + "\n", + "fig, ax = plt.subplots(figsize=(7, 4))\n", + "y_pos = np.arange(len(iv_sorted))\n", + "bar_colors = [group_colors.get(f, \"#999\") for f in iv_sorted[\"feature\"]]\n", + "xerr = (iv_sorted[\"iv_ci_upper\"] - iv_sorted[\"iv_ci_lower\"]) / 2\n", + "\n", + "ax.barh(\n", + " y_pos,\n", + " iv_sorted[\"iv\"],\n", + " xerr=xerr,\n", + " height=0.8,\n", + " color=bar_colors,\n", + " edgecolor=\"white\",\n", + " capsize=3,\n", + " error_kw={\"linewidth\": 1, \"color\": \"#333\"},\n", + ")\n", + "ax.set_yticks(y_pos)\n", + "ax.set_yticklabels([pretty(f) for f in iv_sorted[\"feature\"]], fontsize=9)\n", + "ax.set_xlabel(\"Information Value\")\n", + "ax.set_title(r\"IV with 95\\% Confidence Intervals (Delta Method)\")\n", + "\n", + "for thr, lab in [(0.02, \"Weak\"), (0.1, \"Medium\"), (0.3, \"Strong\")]:\n", + " ax.axvline(thr, color=\"grey\", ls=\":\", lw=0.8, alpha=0.6)\n", + " ax.text(thr, len(iv_sorted) - 0.2, f\" {lab}\", fontsize=7, color=\"grey\", va=\"top\")\n", + "\n", + "legend_patches = [\n", + " Patch(color=C_CB, label=\"Credit Bureau\"),\n", + " Patch(color=C_USER, label=\"Financial / Behaviour\"),\n", + " Patch(color=C_CAT, label=\"Categorical / Profile\"),\n", + "]\n", + "ax.legend(\n", + " handles=legend_patches, loc=\"lower right\", bbox_to_anchor=(0.95, 0), frameon=False, fontsize=8\n", + ")\n", + "ax.spines[[\"top\", \"right\"]].set_visible(False)\n", + "plt.tight_layout()\n", + "plt.savefig(IMAGES / \"iv_bar_chart.png\", dpi=200, bbox_inches=\"tight\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "40e99f1e", + "metadata": {}, + "source": [ + "## 4. Model performance and stability\n", + "\n", + "FastWoe fits logistic regression on WoE-transformed features internally. We examine Gini, DR vs PD over time, and Gini stability by week.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "0d83971d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-12T15:32:24.304752Z", + "iopub.status.busy": "2026-06-12T15:32:24.304522Z", + "iopub.status.idle": "2026-06-12T15:32:24.403456Z", + "shell.execute_reply": "2026-06-12T15:32:24.398631Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train Gini: 40.30%\n", + "Test Gini: 44.82%\n" + ] + } + ], + "source": [ + "# -------------------------------------------------------------------------------\n", + "# Gini (train / test)\n", + "# -------------------------------------------------------------------------------\n", + "def gini_score(y_true, y_pred) -> float:\n", + " \"\"\"Gini coefficient as a number between -1 and 1.\"\"\"\n", + " return 2 * roc_auc_score(y_true, y_pred) - 1\n", + "\n", + "\n", + "y_pred_train = pipeline.predict_proba(X_train)[:, 1]\n", + "y_pred_test = pipeline.predict_proba(X_test)[:, 1]\n", + "\n", + "gini_train = gini_score(y_train, y_pred_train)\n", + "gini_test = gini_score(y_test, y_pred_test)\n", + "print(f\"Train Gini: {gini_train:.2%}\")\n", + "print(f\"Test Gini: {gini_test:.2%}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "cc78af34", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-12T15:32:24.429043Z", + "iopub.status.busy": "2026-06-12T15:32:24.428798Z", + "iopub.status.idle": "2026-06-12T15:32:25.058523Z", + "shell.execute_reply": "2026-06-12T15:32:25.057684Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 470, + "width": 630 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# -------------------------------------------------------------------------------\n", + "# Figure: DR vs PD over time\n", + "# -------------------------------------------------------------------------------\n", + "df[\"prediction\"] = woe_encoder.predict_proba(X)[:, 1]\n", + "df[\"prediction\"] = pipeline.predict_proba(X)[:, 1]\n", + "\n", + "df[\"is_default\"].groupby(df[\"date_decision\"]).mean().plot(\n", + " kind=\"line\",\n", + " color=C_DR,\n", + " linewidth=0.8,\n", + " label=\"DR\",\n", + " rot=45,\n", + " title=\"Through-the-economic-cycle split by date of decision\",\n", + ")\n", + "df[\"prediction\"].groupby(df[\"date_decision\"]).mean().plot(\n", + " kind=\"line\",\n", + " color=C_PD,\n", + " linewidth=0.8,\n", + " label=\"PD\",\n", + " rot=45,\n", + ")\n", + "plt.axhline(y=df[\"is_default\"].mean(), color=\"r\", linestyle=\"--\", linewidth=0.5, label=\"TTC DR\")\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.savefig(IMAGES / \"dr_vs_pd_over_time.png\", dpi=200, bbox_inches=\"tight\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c936d4f2", + "metadata": {}, + "source": [ + "## 5. PSI drift inference\n", + "\n", + "PSI is the same Jeffreys divergence computed across time windows. With standard errors, we can formally test whether observed drift is statistically significant or just sampling noise.\n", + "\n", + "$$\\text{PSI} = \\sum_j (q_j - p_j)\\ln\\frac{q_j}{p_j}, \\qquad \\text{SE}(\\text{PSI}) = \\sqrt{\\sum_j (q_j - p_j)^2 \\left(\\frac{1}{n^{\\text{ref}}_j} + \\frac{1}{n^{\\text{comp}}_j}\\right)}$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "079b609b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-12T15:32:25.059871Z", + "iopub.status.busy": "2026-06-12T15:32:25.059789Z", + "iopub.status.idle": "2026-06-12T15:32:25.104375Z", + "shell.execute_reply": "2026-06-12T15:32:25.103958Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PSI for num_credit_inquiries (reference: weeks [50, 51, 52, 53, 54, 55, 56, 57, 58, 59])\n", + "Minimum weekly application count: 82 (week 68)\n", + "Weeks with significant drift: 32 / 42\n" + ] + }, + { + "data": { + "text/plain": [ + "[Text(0.5, 44.59910197328594, 'Week'),\n", + " Text(8.888888888888918, 0.5, 'PSI'),\n", + " Text(0.5, 1.0, 'PSI Drift Inference: Num credit inquiries')]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# -------------------------------------------------------------------------------\n", + "# PSI over time for income_type — include all weeks\n", + "# -------------------------------------------------------------------------------\n", + "psi_feat = \"num_credit_inquiries\"\n", + "weeks = sorted(df[\"WEEK_NUM\"].unique())\n", + "ref_weeks = weeks[:10]\n", + "\n", + "series = df[psi_feat]\n", + "if series.dtype == object:\n", + "\n", + " def binner(s):\n", + " \"\"\"Bin WoE-encoded categorical features.\"\"\"\n", + " return s.fillna(\"NA\").astype(str)\n", + "elif series.nunique() > 10:\n", + " _, edges = pd.qcut(series.dropna(), q=10, duplicates=\"drop\", retbins=True)\n", + " edges[0], edges[-1] = -np.inf, np.inf\n", + "\n", + " def binner(s):\n", + " \"\"\"Bin numeric features.\"\"\"\n", + " return pd.cut(s.fillna(-999), bins=edges).astype(str)\n", + "else:\n", + "\n", + " def binner(s):\n", + " \"\"\"Bin numeric feature as string.\"\"\"\n", + " return s.fillna(-999).astype(str)\n", + "\n", + "\n", + "ref_counts = binner(df.loc[df[\"WEEK_NUM\"].isin(ref_weeks), psi_feat]).value_counts()\n", + "\n", + "psi_rows = []\n", + "# Include reference weeks as PSI=0\n", + "psi_rows.extend(\n", + " {\"week\": w, \"psi\": 0.0, \"se\": 0.0, \"z\": 0.0, \"significant\": False} for w in ref_weeks\n", + ")\n", + "for w in weeks:\n", + " if w in ref_weeks:\n", + " continue\n", + " ws = df.loc[df[\"WEEK_NUM\"] == w, psi_feat]\n", + " if len(ws) < 50:\n", + " continue\n", + " wc = binner(ws).value_counts()\n", + " all_bins = sorted(set(ref_counts.index) | set(wc.index))\n", + " r = np.array([ref_counts.get(b, 0) for b in all_bins], float)\n", + " c = np.array([wc.get(b, 0) for b in all_bins], float)\n", + " p = np.clip(r / r.sum(), 1e-15, None)\n", + " q = np.clip(c / c.sum(), 1e-15, None)\n", + " psi = float(((q - p) * np.log(q / p)).sum())\n", + " se_ln = np.sqrt(1 / np.clip(r, 1, None) + 1 / np.clip(c, 1, None))\n", + " se = float(np.sqrt(((q - p) ** 2 * se_ln**2).sum()))\n", + " z = psi / se if se > 0 else 0\n", + " psi_rows.append({\"week\": w, \"psi\": psi, \"se\": se, \"z\": z, \"significant\": z > 1.96})\n", + "\n", + "psi_df = pd.DataFrame(psi_rows)\n", + "week_volumes = df.groupby(\"WEEK_NUM\").size()\n", + "print(f\"PSI for {psi_feat} (reference: weeks {ref_weeks})\")\n", + "print(f\"Minimum weekly application count: {week_volumes.min():,} (week {week_volumes.idxmin()})\")\n", + "print(f\"Weeks with significant drift: {psi_df['significant'].sum()} / {len(psi_df)}\")\n", + "psi_df.head(10)\n", + "\n", + "# -------------------------------------------------------------------------------\n", + "# Alternative PSI over time with dates\n", + "# -------------------------------------------------------------------------------\n", + "# Map each week number to its first date in that week\n", + "week_to_date = (\n", + " df.groupby(\"WEEK_NUM\")[\"date_decision\"]\n", + " .min()\n", + " .reset_index()\n", + " .rename(columns={\"date_decision\": \"week_date\"})\n", + ")\n", + "week_to_date[\"week_date\"] = pd.to_datetime(week_to_date[\"week_date\"]) # ensure datetime\n", + "psi_df = psi_df.merge(week_to_date, left_on=\"week\", right_on=\"WEEK_NUM\", how=\"left\")\n", + "\n", + "ref_dates = week_to_date.loc[week_to_date[\"WEEK_NUM\"].isin(ref_weeks), \"week_date\"]\n", + "\n", + "# Add secondary x-axis with dates\n", + "ax2 = ax.twiny()\n", + "ax2.set_xlim(ax.get_xlim())\n", + "\n", + "# Use same tick positions as primary axis\n", + "week_ticks = ax.get_xticks()\n", + "week_ticks = [w for w in week_ticks if psi_df[\"week\"].min() <= w <= psi_df[\"week\"].max()]\n", + "\n", + "# Map week numbers → dates\n", + "tick_dates = [\n", + " week_to_date.set_index(\"WEEK_NUM\")[\"week_date\"].get(int(w), pd.NaT) for w in week_ticks\n", + "]\n", + "tick_labels = [d.strftime(\"%d %b %y\") if pd.notna(d) else \"\" for d in tick_dates]\n", + "\n", + "ax2.set_xticks(week_ticks)\n", + "ax2.set_xticklabels(tick_labels, fontsize=7, rotation=35, ha=\"left\")\n", + "ax2.spines[[\"top\", \"right\"]].set_visible(False)\n", + "\n", + "ax.set(xlabel=\"Week\", ylabel=\"PSI\", title=f\"PSI Drift Inference: {pretty(psi_feat)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5ec8298f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-12T15:32:25.105805Z", + "iopub.status.busy": "2026-06-12T15:32:25.105719Z", + "iopub.status.idle": "2026-06-12T15:32:25.246645Z", + "shell.execute_reply": "2026-06-12T15:32:25.245854Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "image/png": { + "height": 438, + "width": 981 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# -------------------------------------------------------------------------------\n", + "# Figure: PSI over time with CI bands and threshold zones\n", + "# -------------------------------------------------------------------------------\n", + "fig, ax = plt.subplots(figsize=(10, 4.5))\n", + "\n", + "alpha = 0.05\n", + "z = norm.ppf(1 - alpha / 2)\n", + "\n", + "# Shade threshold zones\n", + "ax.axhspan(0, 0.1, color=\"#e4ede4\", alpha=0.5)\n", + "ax.axhspan(0.1, 0.25, color=\"#f7ecd6\", alpha=0.5)\n", + "ax.axhspan(0.25, 0.5, color=\"#f2dee1\", alpha=0.5)\n", + "\n", + "ax.plot(psi_df[\"week\"], psi_df[\"psi\"], \"o-\", color=C_PD, ms=4, lw=1.2)\n", + "ax.fill_between(\n", + " psi_df[\"week\"],\n", + " (psi_df[\"psi\"] - z * psi_df[\"se\"]).clip(lower=0),\n", + " psi_df[\"psi\"] + z * psi_df[\"se\"],\n", + " alpha=0.2,\n", + " color=C_PD,\n", + " label=r\"95\\% CI\",\n", + ")\n", + "\n", + "ax.axhline(0.1, color=\"#e3b638\", ls=\"--\", lw=1)\n", + "ax.axhline(0.25, color=\"#c0566d\", ls=\"--\", lw=1)\n", + "\n", + "ax.text(psi_df[\"week\"].max() + 0.3, 0.04, \"Stable\", fontsize=8, color=\"#5e8b6f\", va=\"center\")\n", + "ax.text(psi_df[\"week\"].max() - 0.8, 0.17, \"Minor shift\", fontsize=8, color=\"#b8922a\", va=\"center\")\n", + "ax.text(psi_df[\"week\"].max() - 0.8, 0.27, \"Major shift\", fontsize=8, color=\"#c0566d\", va=\"center\")\n", + "\n", + "ax.axvspan(ref_weeks[0], ref_weeks[-1], color=\"#ddd\", alpha=0.4, label=\"Reference period\")\n", + "\n", + "ax.legend(bbox_to_anchor=(0.35, 1.105), loc=\"upper left\", frameon=False, fontsize=10, ncol=2)\n", + "\n", + "week_date_map = week_to_date.set_index(\"WEEK_NUM\")[\"week_date\"]\n", + "\n", + "week_ticks = [w for w in ax.get_xticks() if psi_df[\"week\"].min() <= w <= psi_df[\"week\"].max()]\n", + "\n", + "date_labels = []\n", + "for w in week_ticks:\n", + " date = week_date_map.get(int(w), None)\n", + " date_labels.append(date.strftime(\"%d %b %y\") if date is not None else \"\")\n", + "\n", + "ax.set_xticks(week_ticks)\n", + "ax.set_ylim(0, 0.3)\n", + "ax.set_xticklabels(date_labels, fontsize=10, rotation=0, ha=\"center\")\n", + "ax.set_title(f\"PSI Drift Inference: {pretty(psi_feat)}\", fontsize=14, pad=10, y=1.1)\n", + "ax.spines[[\"top\", \"right\"]].set_visible(False)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(IMAGES / \"psi_over_time.png\", dpi=200, bbox_inches=\"tight\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "fb3a17f0", + "metadata": {}, + "source": [ + "## 6. Fairness as dual IV (extension)\n", + "\n", + "The same Jeffreys divergence can measure demographic bias: compute IV of each feature against a protected attribute (e.g. sex) instead of the target. The paper (Section 6) formalises this as:\n", + "\n", + "- **Maximise** $\\text{IV}_\\text{perf}$ (predictive power)\n", + "- **Minimise** $\\text{IV}_\\text{fair}$ (fairness)\n", + "\n", + "With standard errors, hard thresholds become probabilistic: $P(\\text{IV}_\\text{fair} \\leq \\epsilon) \\geq 95\\%$.\n", + "\n", + "We show the computation here; the full Pareto frontier and MIP optimisation are in the [paper](https://arxiv.org/abs/2509.09855).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "63f0f54b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-12T15:32:25.247776Z", + "iopub.status.busy": "2026-06-12T15:32:25.247689Z", + "iopub.status.idle": "2026-06-12T15:32:25.497863Z", + "shell.execute_reply": "2026-06-12T15:32:25.497505Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fairness threshold: IV_demo <= 0.05 at 95% confidence\n", + "Features flagged: 5 / 8\n", + "\n", + " feature iv_pred iv_demo iv_se_demo p_fair\n", + " income_type 0.1095 0.3450 0.0083 5.488996e-277\n", + " marital_status 0.0229 0.1762 0.0079 9.591272e-58\n", + " loan_request_type 0.1196 0.0793 0.0038 6.264996e-15\n", + " family_status 0.0183 0.0780 0.0043 3.717034e-11\n", + "pct_late_installments 0.2292 0.0465 0.0029 8.862640e-01\n" + ] + } + ], + "source": [ + "# -------------------------------------------------------------------------------\n", + "# Predictive IV vs Demographic IV (vs. sex) for each feature\n", + "# -------------------------------------------------------------------------------\n", + "# Fit a separate FastWoe against sex to get demographic IV + SE\n", + "woe_fair = FastWoe(\n", + " binning_method=\"tree\",\n", + " tree_kwargs={\"max_depth\": 3, \"min_samples_leaf\": 50},\n", + ")\n", + "woe_fair.fit(X_train, sex_binary.loc[ix_train])\n", + "fair_analysis = woe_fair.get_iv_analysis()\n", + "\n", + "# Merge predictive and demographic IV\n", + "merged = iv_analysis[[\"feature\", \"iv\", \"iv_se\"]].merge(\n", + " fair_analysis[[\"feature\", \"iv\", \"iv_se\"]],\n", + " on=\"feature\",\n", + " suffixes=(\"_pred\", \"_demo\"),\n", + ")\n", + "\n", + "epsilon = 0.05\n", + "merged[\"p_fair\"] = stats.norm.cdf(\n", + " epsilon,\n", + " loc=merged[\"iv_demo\"],\n", + " scale=merged[\"iv_se_demo\"].clip(lower=1e-15),\n", + ")\n", + "\n", + "flagged = merged[merged[\"p_fair\"] < 0.95].sort_values(\"iv_demo\", ascending=False)\n", + "print(f\"Fairness threshold: IV_demo <= {epsilon} at 95% confidence\")\n", + "print(f\"Features flagged: {len(flagged)} / {len(merged)}\\n\")\n", + "if len(flagged):\n", + " print(flagged[[\"feature\", \"iv_pred\", \"iv_demo\", \"iv_se_demo\", \"p_fair\"]].to_string(index=False))\n", + "else:\n", + " print(\"All features pass the fairness test.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "927b2f8a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-12T15:32:25.498876Z", + "iopub.status.busy": 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" + ] + }, + "metadata": { + "image/png": { + "height": 539, + "width": 684 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# -------------------------------------------------------------------------------\n", + "# Figure: Predictive IV vs Demographic IV with quadrant labels\n", + "# -------------------------------------------------------------------------------\n", + "fig, ax = plt.subplots(figsize=(7, 5.5))\n", + "\n", + "# Shade quadrants\n", + "iv_mid = 0.05 # fairness threshold (y-axis)\n", + "pred_mid = 0.05 # predictive relevance threshold (x-axis)\n", + "xlim = max(merged[\"iv_pred\"].max() * 1.15, 0.25)\n", + "ylim = max(merged[\"iv_demo\"].max() * 1.15, 0.10)\n", + "\n", + "# Bottom-right: high IV, low bias — ideal\n", + "ax.axhspan(0, iv_mid, xmin=0, xmax=1, color=\"#e4ede4\", alpha=0.3)\n", + "# Top: high bias zone\n", + "ax.axhspan(iv_mid, ylim, color=\"#f2dee1\", alpha=0.3)\n", + "\n", + "# Quadrant labels\n", + "ax.text(\n", + " xlim * 0.85,\n", + " iv_mid * 0.7,\n", + " r\"Predictive \\& Fair\",\n", + " fontsize=9,\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " color=\"#5e8b6f\",\n", + " alpha=0.7,\n", + " style=\"italic\",\n", + ")\n", + "ax.text(\n", + " xlim * 0.75,\n", + " iv_mid + (ylim - iv_mid) * 0.5,\n", + " \"Predictive but Biased\",\n", + " fontsize=9,\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " color=\"#c0566d\",\n", + " alpha=0.7,\n", + " style=\"italic\",\n", + ")\n", + "ax.text(\n", + " pred_mid * 0.5,\n", + " iv_mid * 0.7,\n", + " r\"Weak \\& Fair\",\n", + " fontsize=9,\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " color=\"#9a96a0\",\n", + " alpha=0.6,\n", + " style=\"italic\",\n", + ")\n", + "ax.text(\n", + " pred_mid * 0.5,\n", + " iv_mid + (ylim - iv_mid) * 0.5,\n", + " r\"Weak \\& Biased\",\n", + " fontsize=9,\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " color=\"#9a96a0\",\n", + " alpha=0.6,\n", + " style=\"italic\",\n", + ")\n", + "\n", + "# Scatter\n", + "scatter_colors = [group_colors.get(f, \"#999\") for f in merged[\"feature\"]]\n", + "ax.scatter(\n", + " merged[\"iv_pred\"], merged[\"iv_demo\"], c=scatter_colors, s=60, edgecolors=\"white\", zorder=3\n", + ")\n", + "ax.errorbar(\n", + " merged[\"iv_pred\"],\n", + " merged[\"iv_demo\"],\n", + " xerr=1.96 * merged[\"iv_se_pred\"],\n", + " yerr=1.96 * merged[\"iv_se_demo\"],\n", + " fmt=\"none\",\n", + " ecolor=\"#ccc\",\n", + " elinewidth=0.8,\n", + " capsize=2,\n", + " zorder=2,\n", + ")\n", + "\n", + "for _, r in merged.iterrows():\n", + " ax.annotate(\n", + " pretty(r[\"feature\"]),\n", + " (r[\"iv_pred\"], r[\"iv_demo\"]),\n", + " fontsize=7,\n", + " xytext=(4, 4),\n", + " textcoords=\"offset points\",\n", + " )\n", + "\n", + "# Threshold lines\n", + "ax.axhline(iv_mid, color=C_FAIR, ls=\"--\", lw=1, alpha=0.7)\n", + "ax.axvline(pred_mid, color=\"#9a96a0\", ls=\":\", lw=0.8, alpha=0.5)\n", + "\n", + "legend_patches = [\n", + " Patch(color=C_CB, label=\"Credit Bureau\"),\n", + " Patch(color=C_USER, label=\"Financial / Behaviour\"),\n", + " Patch(color=C_CAT, label=\"Categorical / Profile\"),\n", + " plt.Line2D([0], [0], color=C_FAIR, ls=\"--\", label=f\"Reference line ({iv_mid})\"),\n", + "]\n", + "ax.legend(handles=legend_patches, loc=\"upper left\", frameon=False, fontsize=8)\n", + "\n", + "ax.set(\n", + " xlabel=\"Predictive IV (feature vs. target)\",\n", + " ylabel=\"Demographic IV (feature vs. sex)\",\n", + " title=\"Performance-Fairness Trade-Off\",\n", + " xlim=(0, xlim),\n", + " ylim=(0, ylim),\n", + ")\n", + "ax.spines[[\"top\", \"right\"]].set_visible(False)\n", + "plt.tight_layout()\n", + "plt.savefig(IMAGES / \"pareto_predictive_vs_demographic.png\", dpi=200, bbox_inches=\"tight\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "4d705a20", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-12T15:32:25.703170Z", + "iopub.status.busy": "2026-06-12T15:32:25.703037Z", + "iopub.status.idle": "2026-06-12T15:32:25.716602Z", + "shell.execute_reply": "2026-06-12T15:32:25.715889Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sex_738LFM
income_type
EMPLOYED0.1506290.260945
HANDICAPPED_20.0037150.004977
HANDICAPPED_30.0021190.003961
OTHER0.0017190.018283
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SALARIED_GOVT0.3127420.186491
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" + ], + "text/plain": [ + "sex_738L F M\n", + "income_type \n", + "EMPLOYED 0.150629 0.260945\n", + "HANDICAPPED_2 0.003715 0.004977\n", + "HANDICAPPED_3 0.002119 0.003961\n", + "OTHER 0.001719 0.018283\n", + "PRIVATE_SECTOR_EMPLOYEE 0.210777 0.356272\n", + "RETIRED_PENSIONER 0.297114 0.150025\n", + "SALARIED_GOVT 0.312742 0.186491\n", + "SELFEMPLOYED 0.021185 0.019045" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Proportions within each sex (column-normalized): share of each\n", + "# income type among female vs. male applicants.\n", + "pd.crosstab(\n", + " df[\"income_type\"].astype(str),\n", + " df[\"sex_738L\"],\n", + " normalize=\"columns\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "abb39a42", + "metadata": {}, + "source": [ + "### IV density by demographic group\n", + "\n", + "A more nuanced fairness test: compute IV _separately_ for each sex group on shared bins, then test whether the _difference_ in IV is significant. The delta method gives SE for each group's IV; the SE of the difference is $\\sqrt{\\text{SE}_0^2 + \\text{SE}_1^2}$.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "be9d2a2d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-12T15:32:25.718386Z", + "iopub.status.busy": "2026-06-12T15:32:25.718215Z", + "iopub.status.idle": "2026-06-12T15:32:25.774494Z", + "shell.execute_reply": "2026-06-12T15:32:25.773752Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Feature: pct_late_installments\n", + "Female (n=25,780): IV = 0.3057 +/- 0.0216\n", + "Male (n=16,028): IV = 0.2147 +/- 0.0204\n", + "Difference: dIV = -0.0910 +/- 0.0297\n", + "Z = -3.07, p = 0.0022 (significant)\n" + ] + } + ], + "source": [ + "# -------------------------------------------------------------------------------\n", + "# IV by demographic group for a borderline feature\n", + "# -------------------------------------------------------------------------------\n", + "test_feat = \"pct_late_installments\"\n", + "sex_col = df[\"sex_738L\"]\n", + "\n", + "# Shared bins from the full training data\n", + "feat_series = X_train[test_feat].astype(float)\n", + "n_bins = 5\n", + "if feat_series.nunique() > n_bins:\n", + " shared_bins = pd.qcut(feat_series.dropna(), q=n_bins, duplicates=\"drop\").cat.categories\n", + "else:\n", + " shared_bins = None\n", + "\n", + "\n", + "def iv_se_with_bins(x, y, bins):\n", + " \"\"\"IV and SE using shared bins.\"\"\"\n", + " binned = pd.cut(x, bins=bins, include_lowest=True)\n", + " tmp = pd.DataFrame({\"bin\": binned, \"y\": y})\n", + " agg = tmp.groupby(\"bin\", observed=False)[\"y\"].agg([\"count\", \"sum\"]).reset_index()\n", + " agg[\"good\"], agg[\"bad\"] = agg[\"sum\"], agg[\"count\"] - agg[\"sum\"]\n", + " total_good, total_bad = agg[\"good\"].sum(), agg[\"bad\"].sum()\n", + " if total_good == 0 or total_bad == 0:\n", + " return 0.0, 0.0\n", + " pg = np.clip(agg[\"good\"] / total_good, 1e-15, None)\n", + " pb = np.clip(agg[\"bad\"] / total_bad, 1e-15, None)\n", + " woe = np.log(pg / pb)\n", + " w = pg - pb\n", + " iv = float((w * woe).sum())\n", + " var_woe = 1 / np.clip(agg[\"good\"], 1, None) + 1 / np.clip(agg[\"bad\"], 1, None)\n", + " se = float(np.sqrt((w**2 * var_woe).sum()))\n", + " return iv, se\n", + "\n", + "\n", + "# Compute IV for each sex group\n", + "mask_f = sex_col.loc[ix_train] == \"F\"\n", + "mask_m = sex_col.loc[ix_train] == \"M\"\n", + "\n", + "iv_f, se_f = iv_se_with_bins(\n", + " X_train.loc[mask_f, test_feat].astype(float), y_train.loc[mask_f], shared_bins\n", + ")\n", + "iv_m, se_m = iv_se_with_bins(\n", + " X_train.loc[mask_m, test_feat].astype(float), y_train.loc[mask_m], shared_bins\n", + ")\n", + "\n", + "iv_diff = iv_m - iv_f\n", + "se_diff = np.sqrt(se_f**2 + se_m**2)\n", + "z_diff = iv_diff / se_diff if se_diff > 0 else 0\n", + "p_diff = 2 * (1 - stats.norm.cdf(abs(z_diff)))\n", + "\n", + "n_f, n_m = mask_f.sum(), mask_m.sum()\n", + "print(f\"Feature: {test_feat}\")\n", + "print(f\"Female (n={n_f:,}): IV = {iv_f:.4f} +/- {se_f:.4f}\")\n", + "print(f\"Male (n={n_m:,}): IV = {iv_m:.4f} +/- {se_m:.4f}\")\n", + "print(f\"Difference: dIV = {iv_diff:.4f} +/- {se_diff:.4f}\")\n", + "print(\n", + " f\"Z = {z_diff:.2f}, p = {p_diff:.4f} {'(significant)' if p_diff < 0.05 else '(not significant)'}\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f46ca20e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-12T15:32:25.775991Z", + "iopub.status.busy": "2026-06-12T15:32:25.775853Z", + "iopub.status.idle": "2026-06-12T15:32:26.165659Z", + "shell.execute_reply": "2026-06-12T15:32:26.165005Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "image/png": { + "height": 440, + "width": 1189 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# -------------------------------------------------------------------------------\n", + "# Figure: IV density by group + difference distribution\n", + "# -------------------------------------------------------------------------------\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4.5))\n", + "\n", + "# Left: IV distributions for each group\n", + "iv_min = min(iv_f - 4 * se_f, iv_m - 4 * se_m)\n", + "iv_max = max(iv_f + 4 * se_f, iv_m + 4 * se_m)\n", + "x = np.linspace(max(0, iv_min), iv_max, 500)\n", + "\n", + "pdf_f = stats.norm.pdf(x, iv_f, se_f)\n", + "pdf_m = stats.norm.pdf(x, iv_m, se_m)\n", + "\n", + "ax1.plot(x, pdf_f, color=C_DR, lw=2.5, label=f\"Female (n={n_f:,})\")\n", + "ax1.fill_between(x, pdf_f, alpha=0.2, color=C_DR)\n", + "ax1.plot(x, pdf_m, color=C_PD, lw=2.5, label=f\"Male (n={n_m:,})\")\n", + "ax1.fill_between(x, pdf_m, alpha=0.2, color=C_PD)\n", + "\n", + "ax1.axvline(iv_f, color=C_DR, ls=\"--\", lw=1, alpha=0.6)\n", + "ax1.axvline(iv_m, color=C_PD, ls=\"--\", lw=1, alpha=0.6)\n", + "\n", + "for thr, lab, c in [(0.02, \"Weak\", \"#9a96a0\"), (0.1, \"Medium\", \"#e3b638\"), (0.3, \"Strong\", \"#c0566d\")]:\n", + " if thr <= iv_max:\n", + " ax1.axvline(thr, color=c, ls=\":\", alpha=0.5, lw=1)\n", + " ax1.text(\n", + " thr,\n", + " max(pdf_f.max(), pdf_m.max()) * 1.05,\n", + " f\" {lab}\",\n", + " rotation=90,\n", + " va=\"top\",\n", + " ha=\"right\",\n", + " color=c,\n", + " fontsize=8,\n", + " )\n", + "\n", + "test_feat_name = \"Percent Late Installments\"\n", + "ax1.set(xlabel=\"Information Value\", ylabel=\"Density\", title=f\"{test_feat_name}: IV by Sex\")\n", + "ax1.legend(frameon=False, fontsize=9, loc=\"lower left\")\n", + "ax1.spines[[\"top\", \"right\"]].set_visible(False)\n", + "\n", + "# Right: difference distribution\n", + "x_diff = np.linspace(iv_diff - 4 * se_diff, iv_diff + 4 * se_diff, 500)\n", + "pdf_diff = stats.norm.pdf(x_diff, iv_diff, se_diff)\n", + "\n", + "ax2.plot(x_diff, pdf_diff, color=C_FAIR, lw=2.5)\n", + "ax2.fill_between(x_diff, pdf_diff, alpha=0.3, color=C_FAIR)\n", + "ax2.axvline(0, color=\"black\", lw=1.5, alpha=0.7)\n", + "ax2.axvline(iv_diff, color=C_FAIR, ls=\"--\", lw=1.5)\n", + "\n", + "ci_lo = iv_diff - 1.96 * se_diff\n", + "ci_hi = iv_diff + 1.96 * se_diff\n", + "ax2.axvline(ci_lo, color=C_FAIR, ls=\":\", alpha=0.5)\n", + "ax2.axvline(ci_hi, color=C_FAIR, ls=\":\", alpha=0.5)\n", + "\n", + "sig = \"***\" if p_diff < 0.001 else \"**\" if p_diff < 0.01 else \"*\" if p_diff < 0.05 else \"ns\"\n", + "ax2.set(\n", + " xlabel=r\"$\\Delta$IV (Male $-$ Female)\",\n", + " ylabel=\"Density\",\n", + " title=rf\"Difference: {iv_diff:.4f} $\\pm$ {se_diff:.4f} ({sig}, p={p_diff:.4f})\",\n", + ")\n", + "ax2.spines[[\"top\", \"right\"]].set_visible(False)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(IMAGES / \"iv_density_by_group.png\", dpi=200, bbox_inches=\"tight\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "3b38575f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-12T15:32:26.167006Z", + "iopub.status.busy": "2026-06-12T15:32:26.166913Z", + "iopub.status.idle": "2026-06-12T15:32:26.355279Z", + "shell.execute_reply": "2026-06-12T15:32:26.354869Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 390, + "width": 490 + } + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "pct_late_installments:\n", + "IV_perf = 0.2292 +/- 0.0181\n", + "IV_fair = 0.0465 +/- 0.0029\n", + "R_nominal = 4.93\n", + "R_conservative = 3.38 (at 99% CI)\n" + ] + } + ], + "source": [ + "# -------------------------------------------------------------------------------\n", + "# Figure: Nominal vs Conservative performance-fairness ratio\n", + "# R = IV_perf / IV_fair vs R_conservative = (IV_perf - k*SE) / (IV_fair + k*SE)\n", + "# -------------------------------------------------------------------------------\n", + "row = merged[merged[\"feature\"] == test_feat].iloc[0]\n", + "iv_perf, se_perf = row[\"iv_pred\"], row[\"iv_se_pred\"]\n", + "iv_fair, se_fair = row[\"iv_demo\"], row[\"iv_se_demo\"]\n", + "\n", + "alpha = 0.01\n", + "k = stats.norm.ppf(1 - alpha / 2)\n", + "\n", + "r_nominal = iv_perf / iv_fair\n", + "r_conservative = (iv_perf - k * se_perf) / (iv_fair + k * se_fair)\n", + "\n", + "fig, ax = plt.subplots(figsize=(5, 4))\n", + "bars = ax.bar(\n", + " [\"Nominal\", f\"Conservative\\n({100 * (1 - alpha):.0f}\\\\% CI, k={k:.2f})\"],\n", + " [r_nominal, r_conservative],\n", + " color=[C_PD, C_FAIR],\n", + " alpha=0.7,\n", + " edgecolor=\"black\",\n", + " linewidth=0.8,\n", + " width=0.5,\n", + ")\n", + "ax.axhline(y=1, color=\"grey\", linestyle=\"--\", linewidth=0.8, label=\"R = 1 (break-even)\")\n", + "ax.set_ylabel(\"Performance-Fairness Ratio (R)\")\n", + "ax.set_title(f\"Confidence-Adjusted Trade-Off Ratio\\n{pretty(test_feat)}\")\n", + "ax.set_ylim(0, max(r_nominal, r_conservative) * 1.3)\n", + "\n", + "for bar, val in zip(bars, [r_nominal, r_conservative]):\n", + " ax.text(\n", + " bar.get_x() + bar.get_width() / 2,\n", + " val + 0.05,\n", + " f\"{val:.2f}\",\n", + " ha=\"center\",\n", + " va=\"bottom\",\n", + " fontsize=11,\n", + " )\n", + "\n", + "ax.legend(frameon=False, fontsize=8)\n", + "ax.spines[[\"top\", \"right\"]].set_visible(False)\n", + "plt.tight_layout()\n", + "plt.savefig(IMAGES / \"conservative_ratio.png\", dpi=200, bbox_inches=\"tight\")\n", + "plt.show()\n", + "\n", + "print(f\"\\n{test_feat}:\")\n", + "print(f\"IV_perf = {iv_perf:.4f} +/- {se_perf:.4f}\")\n", + "print(f\"IV_fair = {iv_fair:.4f} +/- {se_fair:.4f}\")\n", + "print(f\"R_nominal = {r_nominal:.2f}\")\n", + "print(f\"R_conservative = {r_conservative:.2f} (at {100 * (1 - alpha):.0f}% CI)\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.11.8)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/foundation-frontiers/posts/2026/08/18/references.bib b/foundation-frontiers/posts/2026/08/18/references.bib new file mode 100644 index 00000000..102d32c4 --- /dev/null +++ b/foundation-frontiers/posts/2026/08/18/references.bib @@ -0,0 +1,67 @@ +@article{sudjianto2025information, + title={An Information-Theoretic Framework for Credit Risk Modeling: Unifying Industry Practice with Statistical Theory for Fair and Interpretable Scorecards}, + author={Sudjianto, Agus and Burakov, Denis}, + journal={arXiv preprint arXiv:2509.09855}, + year={2025} +} + +@article{shannon1948mathematical, + title={A mathematical theory of communication}, + author={Shannon, Claude E.}, + journal={Bell System Technical Journal}, + volume={27}, + number={3}, + pages={379--423}, + year={1948} +} + +@article{kullback1951information, + title={On information and sufficiency}, + author={Kullback, Solomon and Leibler, Richard A.}, + journal={Annals of Mathematical Statistics}, + volume={22}, + number={1}, + pages={79--86}, + year={1951} +} + +@book{good1950probability, + title={Probability and the Weighing of Evidence}, + author={Good, Irving John}, + year={1950}, + publisher={Charles Griffin \& Company} +} + +@book{siddiqi2017intelligent, + title={Intelligent Credit Scoring: Building and Implementing Better Credit Risk Scorecards}, + author={Siddiqi, Naeem}, + edition={2}, + year={2017}, + publisher={Wiley} +} + +@misc{homecredit2024, + author = {Daniel Herman and Tomas Jelinek and Walter Reade and Maggie Demkin and Addison Howard}, + title = {Home Credit - Credit Risk Model Stability}, + year = {2024}, + howpublished = {\url{https://kaggle.com/competitions/home-credit-credit-risk-model-stability}}, + note = {Kaggle} +} + +@book{jeffreys1961theory, + title={Theory of Probability}, + author={Jeffreys, Harold}, + edition={3}, + year={1961}, + publisher={Oxford University Press} +} + +@article{hand1997statistical, + title={Statistical classification methods in consumer credit scoring: a review}, + author={Hand, David J. and Henley, William E.}, + journal={Journal of the Royal Statistical Society: Series A}, + volume={160}, + number={3}, + pages={523--541}, + year={1997} +} diff --git a/foundation-frontiers/posts/2026/08/18/scripts/download_data.py b/foundation-frontiers/posts/2026/08/18/scripts/download_data.py new file mode 100644 index 00000000..d91c1936 --- /dev/null +++ b/foundation-frontiers/posts/2026/08/18/scripts/download_data.py @@ -0,0 +1,71 @@ +""" +Download the Home Credit dataset from Kaggle. + +Requirements: + pip install kaggle + +Authentication (pick one): + 1. Set env var: export KAGGLE_API_TOKEN="KGAT_..." + 2. Or place kaggle.json in ~/.kaggle/kaggle.json + +Usage: + python scripts/download_data.py +""" + +from __future__ import annotations + +import os +import subprocess +import sys +import zipfile +from pathlib import Path + +from dotenv import load_dotenv + +COMPETITION: str = "home-credit-credit-risk-model-stability" +DATA_DIR: Path = Path(__file__).resolve().parent.parent / "data" +DEST: Path = DATA_DIR / COMPETITION + + +def load_env_token() -> None: + """Load KAGGLE_API_TOKEN from .env if not already set.""" + if os.environ.get("KAGGLE_API_TOKEN"): + return + load_dotenv(Path(__file__).resolve().parent.parent / ".env") + + +def main() -> None: + load_env_token() + + if ( + not os.environ.get("KAGGLE_API_TOKEN") + and not Path("~/.kaggle/kaggle.json").expanduser().exists() + ): + print("Error: No Kaggle credentials found.") + print("Set KAGGLE_API_TOKEN env var or place kaggle.json in ~/.kaggle/") + sys.exit(1) + + DATA_DIR.mkdir(parents=True, exist_ok=True) + zip_path: Path = DATA_DIR / f"{COMPETITION}.zip" + + if DEST.exists() and any(DEST.rglob("*.parquet")): + print(f"Dataset already exists at {DEST}") + print("Delete the directory to re-download.") + return + + print(f"Downloading {COMPETITION}...") + subprocess.run( + ["kaggle", "competitions", "download", "-c", COMPETITION, "-p", str(DATA_DIR)], + check=True, + ) + + print(f"Extracting to {DEST}...") + with zipfile.ZipFile(zip_path, "r") as zf: + zf.extractall(DEST) + + zip_path.unlink() + print(f"Done. Dataset at {DEST}") + + +if __name__ == "__main__": + main() diff --git a/foundation-frontiers/posts/2026/08/18/scripts/prepare_dataset.py b/foundation-frontiers/posts/2026/08/18/scripts/prepare_dataset.py new file mode 100644 index 00000000..7e04e3ba --- /dev/null +++ b/foundation-frontiers/posts/2026/08/18/scripts/prepare_dataset.py @@ -0,0 +1,213 @@ +""" +Prepare and upload the processed Home Credit dataset to HuggingFace Hub. + +Reads raw parquet files, merges features + demographics, and saves a single +processed parquet. Optionally uploads to HF Hub. + +Prerequisites: + brew install hf + hf auth login + +Usage: + python scripts/prepare_dataset.py # prepare only + python scripts/prepare_dataset.py --upload # prepare + upload + python scripts/prepare_dataset.py --repo deburky/home-credit-credit-risk-model-stability +""" + +from __future__ import annotations + +import argparse +import os +import subprocess +import sys +from pathlib import Path + +import numpy as np +import pandas as pd +from dotenv import load_dotenv + +# ------------------------------------------------------------------------------- +# Paths +# ------------------------------------------------------------------------------- + +PROJECT_ROOT: Path = Path(__file__).resolve().parent.parent +RAW_DATA: Path = PROJECT_ROOT / "data" / "home-credit-credit-risk-model-stability" +PROCESSED_DIR: Path = PROJECT_ROOT / "data" / "processed" +OUTPUT_FILE: Path = PROCESSED_DIR / "home_credit_processed.parquet" + +# ------------------------------------------------------------------------------- +# Feature definitions +# ------------------------------------------------------------------------------- + +FEATURES_DPD: list[str] = [ + "maxdpdfrom6mto36m_3546853P", + "maxdpdlast12m_727P", + "maxdpdlast24m_143P", + "maxdpdlast3m_392P", + "maxdpdlast6m_474P", +] + +FEATURES_USER_PROFILE: list[str] = [ + "numinstls_657L", + "numinstlsallpaid_934L", + "pctinstlsallpaidlat10d_839L", + "totalsettled_863A", + "totaldebt_9A", + "currdebt_22A", + "credamount_770A", + "mobilephncnt_593L", + "homephncnt_628L", + "numactivecreds_622L", + "applicationcnt_361L", + "applications30d_658L", + "applicationscnt_1086L", + "applicationscnt_464L", + "applicationscnt_629L", + "avgdbddpdlast24m_3658932P", + "amtinstpaidbefduel24m_4187115A", + "maxdbddpdlast1m_3658939P", + "maxdbddpdtollast12m_3658940P", + "maxdbddpdtollast6m_4187119P", + "numinstpaidlate1d_3546852L", +] + +FEATURES_CB: list[str] = [ + "numberofqueries_373L", + "days120_123L", + "days180_256L", + "days30_165L", + "days90_310L", + "days360_512L", +] + +ALL_FEATURES: list[str] = FEATURES_DPD + FEATURES_CB + FEATURES_USER_PROFILE + + +def prepare() -> Path: + """Load raw parquets, merge, and save processed dataset.""" + if not RAW_DATA.exists(): + print(f"Raw data not found at {RAW_DATA}") + print("Run: python scripts/download_data.py") + sys.exit(1) + + PROCESSED_DIR.mkdir(parents=True, exist_ok=True) + + # ------------------------------------------------------------------------------- + # Load and merge feature tables + # ------------------------------------------------------------------------------- + print("Loading raw parquet files...") + train_dir: str = str(RAW_DATA / "parquet_files" / "train") + + labels: pd.DataFrame = pd.read_parquet(f"{train_dir}/train_base.parquet") + dpd: pd.DataFrame = pd.read_parquet( + f"{train_dir}/train_static_0_1.parquet", + columns=FEATURES_DPD + FEATURES_USER_PROFILE + ["case_id"], + ) + cb: pd.DataFrame = pd.read_parquet( + f"{train_dir}/train_static_cb_0.parquet", + columns=FEATURES_CB + ["case_id"], + ) + + df: pd.DataFrame = labels.merge(dpd, on="case_id").merge(cb, on="case_id") + + # ------------------------------------------------------------------------------- + # Protected attributes from person table + # ------------------------------------------------------------------------------- + # Demographic and categorical attributes from person_1 + # ------------------------------------------------------------------------------- + print("Adding demographic attributes...") + person: pd.DataFrame = pd.read_parquet( + f"{train_dir}/train_person_1.parquet", + columns=[ + "case_id", "num_group1", + "sex_738L", "birth_259D", + "education_927M", "incometype_1044T", + "familystate_447L", "empl_employedtotal_800L", + "language1_981M", "mainoccupationinc_384A", + ], + ) + person = person[person["num_group1"] == 0].drop(columns=["num_group1"]) + + person["birth_259D"] = pd.to_datetime(person["birth_259D"], errors="coerce") + reference_date: pd.Timestamp = pd.Timestamp("2024-01-01") + person["age"] = ((reference_date - person["birth_259D"]).dt.days / 365.25).round(0) + person = person.drop(columns=["birth_259D"]) + + df = df.merge(person, on="case_id", how="left") + + # ------------------------------------------------------------------------------- + # Credit bureau categoricals from static_cb_0 + # ------------------------------------------------------------------------------- + print("Adding credit bureau attributes...") + cb_cat: pd.DataFrame = pd.read_parquet( + f"{train_dir}/train_static_cb_0.parquet", + columns=[ + "case_id", + "maritalst_385M", # marital status (CB, masked, 6 levels) + "requesttype_4525192L", # DEDUCTION / PENSION / SOCIAL (45% coverage) + "description_5085714M", # product description (2 levels) + ], + ) + df = df.merge(cb_cat, on="case_id", how="left") + + # ------------------------------------------------------------------------------- + # Convert date columns + # ------------------------------------------------------------------------------- + df["date_decision"] = pd.to_datetime(df["date_decision"], errors="coerce").dt.strftime("%Y-%m-%d") + df["MONTH"] = pd.to_datetime(df["MONTH"].astype(str), format="%Y%m").dt.strftime("%Y-%m") + + # ------------------------------------------------------------------------------- + # Save + # ------------------------------------------------------------------------------- + df.to_parquet(OUTPUT_FILE, index=False) + print(f"Saved processed dataset: {OUTPUT_FILE}") + print(f" Shape: {df.shape[0]:,} rows x {df.shape[1]} columns") + print(f" Size: {OUTPUT_FILE.stat().st_size / 1e6:.1f} MB") + return OUTPUT_FILE + + +def upload(repo: str) -> None: + """Upload processed directory to HuggingFace Hub.""" + if not OUTPUT_FILE.exists(): + print("No processed file found. Run prepare() first.") + sys.exit(1) + + load_dotenv(PROJECT_ROOT / ".env") + hf_token: str | None = os.environ.get("HF_WRITE_TOKEN") or os.environ.get("HF_TOKEN") + if not hf_token: + print("Error: Set HF_WRITE_TOKEN in .env or HF_TOKEN env var") + sys.exit(1) + + env: dict[str, str] = {**os.environ, "HF_TOKEN": hf_token} + + print(f"Uploading to {repo}...") + subprocess.run( + ["hf", "upload", repo, str(PROCESSED_DIR), "--repo-type=dataset"], + check=True, + env=env, + ) + print(f"Done. Dataset at https://huggingface.co/datasets/{repo}") + + +def main() -> None: + parser: argparse.ArgumentParser = argparse.ArgumentParser( + description="Prepare and optionally upload Home Credit dataset" + ) + parser.add_argument( + "--upload", action="store_true", help="Upload to HuggingFace Hub after preparing" + ) + parser.add_argument( + "--repo", + default="deburky/home-credit-credit-risk-model-stability", + help="HuggingFace dataset repo (default: deburky/home-credit-credit-risk-model-stability)", + ) + args: argparse.Namespace = parser.parse_args() + + prepare() + + if args.upload: + upload(args.repo) + + +if __name__ == "__main__": + main()