Release v0.1.7

.github/workflows/ci.yml

@@ -11,7 +11,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.9', '3.10', '3.11', '3.12']
+        python-version: ['3.9', '3.10', '3.11', '3.12', '3.13', '3.14']
 
     steps:
     - uses: actions/checkout@v4

.github/workflows/compatibility.yml

@@ -21,6 +21,12 @@ jobs:
           - python-version: '3.12'
             python-version-short: 'py312'
             sklearn-version: '1.6.1'
+          - python-version: '3.13'
+            python-version-short: 'py313'
+            sklearn-version: 'latest'
+          - python-version: '3.14'
+            python-version-short: 'py314'
+            sklearn-version: 'latest'
 
     steps:
     - uses: actions/checkout@v4

CHANGELOG.md

@@ -1,5 +1,40 @@
 # Changelog
 
+## Version 0.1.7 (2026-04-11)
+
+**WOE Recalibration & Somers' D Standard Errors**
+
+### New Features
+
+- **`finetune(X_new, y_new)`**: WOE recalibration without redevelopment
+  - Updates WOE values using new data while preserving bin structure (edges, categories)
+  - Pass a full DataFrame to update all features, or a single-column DataFrame for one
+  - Bins absent from new data retain their original WOE (with warning)
+  - Unknown columns are skipped (with warning)
+  - `update_prior=True` shifts the global prior and warns about stale features
+  - Returns `self` for method chaining
+
+- **Asymptotic Standard Errors of Somers' D**: Uncertainty quantification via Goktas & Oznur (2011)
+  - Contingency-table ASE using per-cell concordant/discordant counts and the delta method
+  - Works for binary, ordinal, and continuous targets (for binary targets Somers' D = Gini)
+  - New `somersd_se()` function in `fastwoe.metrics`
+  - New fields in `feature_stats_` and `get_feature_stats()`: `somersd_se`, `somersd_ci_lower`, `somersd_ci_upper`
+  - Returns NaN gracefully for degenerate inputs
+
+### Internal
+
+- Extracted `_apply_binning_to_column()` helper from `transform()` for reuse by `finetune()`
+- Added `_recalibrate_feature()` private method encapsulating per-feature recalibration logic
+
+### Testing
+
+- Added `TestFinetune` class with 13 test cases covering single-feature, full-DataFrame, numerical/categorical, error handling, prior update, missing bins, transform-after-finetune, and method chaining
+- All 132 tests passing
+
+### Examples
+
+- Added `examples/notebooks/fastwoe_finetuning.ipynb` demonstrating recalibration workflow
+
 ## Version 0.1.6 (2026-01-07)
 
 **Stable Release: Type Safety, Robustness & Code Quality** 🎯

Makefile

@@ -1,6 +1,6 @@
 # FastWoe Development Makefile
 
-.PHONY: help install test lint format typecheck typecheck-strict clean check-all
+.PHONY: help install test test-all lint format typecheck typecheck-strict clean check-all
 
 help:  ## Show this help message
 	@echo "FastWoe Development Commands:"
@@ -10,9 +10,17 @@ help:  ## Show this help message
 install:  ## Install development dependencies
 	uv sync --dev
 
-test:  ## Run tests
+test:  ## Run tests (current Python)
 	uv run pytest
 
+test-all:  ## Run tests across all supported Python versions (3.9 → 3.14)
+	@for ver in 3.9 3.10 3.11 3.12 3.13 3.14; do \
+		echo "\n▶ Python $$ver"; \
+		uv run --python $$ver pytest tests/ -q -m "not compatibility" || exit 1; \
+	done
+	@echo "\n✅ All versions passed"
+
+
 lint:  ## Run linting
 	uv run ruff check fastwoe
 

README.md

@@ -469,7 +469,7 @@ print(summary[['feature', 'monotonic_constraint']])
 - Performance may be slightly different but more interpretable
 - Essential for regulatory compliance in credit scoring
 
-For a complete example, see [examples/monotonic_constraints_example.py](examples/monotonic_constraints_example.py).
+For a complete example, see [examples/scripts/fastwoe_monotonic.py](examples/scripts/fastwoe_monotonic.py).
 
 ## 📋 API Reference
 

docs/somersd_ase.md

@@ -0,0 +1,248 @@
+# Asymptotic Standard Error of Somers' D
+
+Reference: Goktas, A., Oznur, I., 2011. *A Comparison of the Most Commonly Used Measures of Association for Doubly Ordered Square Contingency Tables via Simulation.* Metodoloski zvezki 8 (1), 17-37.
+
+## 1. Setup
+
+Given $n$ paired observations $(y_i, x_i)$, build the contingency table $\mathbf{F}$ where rows correspond to sorted unique values of $Y$ and columns to sorted unique values of $X$.
+
+| Symbol | Definition |
+|--------|-----------|
+| $f_{ij}$ | Cell count: number of observations in row $i$, column $j$ |
+| $a$ | Number of rows (unique $Y$ values) |
+| $b$ | Number of columns (unique $X$ values) |
+| $r_i$ | Row sum: $r_i = \sum_j f_{ij}$ |
+| $W$ | Sample size: $W = \sum_{ij} f_{ij} = n$ |
+
+## 2. Concordant and Discordant Counts
+
+For each cell $(i, j)$ of the contingency table, define:
+
+**$C_{ij}$** = number of observations that would form a **concordant** pair with any observation in cell $(i, j)$. These are observations in cells strictly above-left or strictly below-right:
+
+$$C_{ij} = \sum_{\substack{r > i \\ s > j}} f_{rs} \;+\; \sum_{\substack{r < i \\ s < j}} f_{rs}$$
+
+**$D_{ij}$** = number of observations that would form a **discordant** pair:
+
+$$D_{ij} = \sum_{\substack{r > i \\ s < j}} f_{rs} \;+\; \sum_{\substack{r < i \\ s > j}} f_{rs}$$
+
+These are computed efficiently using 2D prefix sums.
+
+## 3. Somers' D Statistic
+
+Total concordant and discordant pairs:
+
+$$P = \sum_{ij} f_{ij} \cdot C_{ij} \qquad Q = \sum_{ij} f_{ij} \cdot D_{ij}$$
+
+Denominator - number of ordered pairs untied on $Y$:
+
+$$D_r = W^2 - \sum_i r_i^2$$
+
+For binary $Y$ with $m$ positives and $W - m$ negatives: $D_r = 2m(W - m)$.
+
+**Somers' D:**
+
+$$D = \frac{P - Q}{D_r}$$
+
+For binary targets, this equals the Gini coefficient $= 2 \cdot \text{AUC} - 1$.
+
+## 4. Row Midranks
+
+Define the midrank of row $i$ as the average position of observations in that row:
+
+$$R_i = \sum_{k=1}^{i} r_k + \frac{1 - r_i}{2}$$
+
+This places each row at the center of its range in the marginal ranking.
+
+For example, if $r = [3, 3, 3]$:
+
+- $R_1 = 3 + (1-3)/2 = 2$
+- $R_2 = 6 + (1-3)/2 = 5$
+- $R_3 = 9 + (1-3)/2 = 8$
+
+## 5. ASE via the Delta Method
+
+Somers' D is a **ratio of two U-statistics**: $D = (P - Q) / D_r$.
+
+To get the variance of a ratio $f/g$, the delta method gives:
+
+$$\text{Var}\!\left(\frac{f}{g}\right) \approx \frac{1}{g^2} \cdot \text{Var}(f - D \cdot g)$$
+
+For each cell $(i, j)$, the **linearized residual** has two parts:
+
+### Part 1: Numerator contribution (scaled)
+
+$$D_r \cdot (C_{ij} - D_{ij})$$
+
+This is how much cell $(i, j)$ contributes to the net concordance, scaled by the denominator.
+
+### Part 2: Denominator correction
+
+$$(P - Q) \cdot (W - R_i)$$
+
+Here $(W - R_i)$ captures how much row $i$ contributes to $D_r$ (pairs untied on $Y$). The factor $(P - Q) = D \cdot D_r$ weights this by the current value of $D$.
+
+### Combined residual per cell
+
+$$\varepsilon_{ij} = D_r \cdot (C_{ij} - D_{ij}) \;-\; (P - Q) \cdot (W - R_i)$$
+
+Note that $R_i$ depends only on the row, not the column - the denominator $D_r$ is determined entirely by the $Y$ marginal distribution.
+
+### ASE formula
+
+The weighted sum of squared residuals, normalized:
+
+$$\boxed{\text{ASE}_1 = \frac{2}{D_r^2} \sqrt{\sum_{ij} f_{ij} \cdot \varepsilon_{ij}^2}}$$
+
+Expanding:
+
+$$\text{ASE}_1 = \frac{2}{D_r^2} \sqrt{\sum_{ij} f_{ij} \left\{ D_r(C_{ij} - D_{ij}) - (P - Q)(W - R_i) \right\}^2}$$
+
+The factor of 2 accounts for the symmetry of ordered pairs (each unordered pair is counted twice).
+
+## 6. ASE Under the Null ($\text{ASE}_0$)
+
+Under $H_0\!: D = 0$, the denominator correction term vanishes (since $P - Q = 0$), giving a simpler formula:
+
+$$\boxed{\text{ASE}_0 = \frac{2}{D_r} \sqrt{\sum_{ij} f_{ij} (C_{ij} - D_{ij})^2 \;-\; \frac{(P - Q)^2}{W}}}$$
+
+This is used for hypothesis testing (is $D$ significantly different from zero?).
+
+## 7. Summary
+
+| Quantity | Formula | Use |
+|----------|---------|-----|
+| $D$ | $(P - Q) \;/\; D_r$ | Point estimate |
+| $\text{ASE}_1$ | $\frac{2}{D_r^2} \sqrt{\sum f_{ij} \cdot \varepsilon_{ij}^2}$ | Confidence intervals |
+| $\text{ASE}_0$ | $\frac{2}{D_r} \sqrt{\sum f_{ij}(C_{ij}-D_{ij})^2 - (P-Q)^2/W}$ | Hypothesis testing |
+
+For a 95% confidence interval:
+
+$$D \;\pm\; z_{0.975} \cdot \text{ASE}_1$$
+
+For a two-sided test of $H_0\!: D = 0$:
+
+$$z = \frac{D}{\text{ASE}_0}$$
+
+## 8. Worked Example
+
+A small binary credit risk table: 10 applicants, target $Y \in \{0, 1\}$, WOE-encoded feature $X \in \{1, 2, 3\}$ (three bins).
+
+```python
+import numpy as np
+
+#-----------------------------------------------------------------------------------------------
+# Data
+#-----------------------------------------------------------------------------------------------
+y = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1], dtype=float)
+x = np.array([1, 1, 2, 2, 3, 1, 2, 3, 3, 3], dtype=float)
+
+#-----------------------------------------------------------------------------------------------
+# Step 1: Contingency table
+#-----------------------------------------------------------------------------------------------
+#          x=1  x=2  x=3
+#   y=0  [  2    2    1 ]
+#   y=1  [  1    1    3 ]
+CT = np.array([[2, 2, 1],
+               [1, 1, 3]], dtype=float)
+
+a, b = CT.shape          # 2 rows, 3 columns
+W = CT.sum()              # 10
+r = CT.sum(axis=1)        # [5, 5]
+
+print(f"CT:\n{CT}")
+print(f"W={W}, r={r}")
+
+#-----------------------------------------------------------------------------------------------
+# Step 2: Concordant (C) and discordant (D) matrices
+#-----------------------------------------------------------------------------------------------
+# C[i,j] = sum of cells strictly below-right + strictly above-left
+# D[i,j] = sum of cells strictly below-left  + strictly above-right
+C = np.zeros_like(CT)
+D = np.zeros_like(CT)
+for i in range(a):
+    for j in range(b):
+        C[i, j] = CT[i+1:, j+1:].sum() + CT[:i, :j].sum()
+        D[i, j] = CT[i+1:, :j].sum()  + CT[:i, j+1:].sum()
+
+print(f"\nC (concordant counts):\n{C}")
+print(f"D (discordant counts):\n{D}")
+
+#-----------------------------------------------------------------------------------------------
+# Step 3: Somers' D
+#-----------------------------------------------------------------------------------------------
+P  = (CT * C).sum()                # total concordant
+Q  = (CT * D).sum()                # total discordant
+Dr = W**2 - (r**2).sum()           # pairs untied on Y
+
+D_val = (P - Q) / Dr
+
+print(f"\nP={P}, Q={Q}, Dr={Dr}")
+print(f"Somers' D = (P-Q)/Dr = {D_val:.6f}")
+
+#-----------------------------------------------------------------------------------------------
+# Step 4: Row midranks
+#-----------------------------------------------------------------------------------------------
+RR = np.cumsum(r) + (1.0 - r) / 2.0
+print(f"\nRow midranks R = {RR}")
+
+#-----------------------------------------------------------------------------------------------
+# Step 5: ASE (delta method)
+#-----------------------------------------------------------------------------------------------
+RR_mat = np.repeat(RR[:, np.newaxis], b, axis=1)
+
+# Linearized residual per cell
+eps = Dr * (C - D) - (P - Q) * (W - RR_mat)
+print(f"\nResiduals eps:\n{eps}")
+
+ASE = 2.0 / Dr**2 * np.sqrt((CT * eps**2).sum())
+print(f"\nASE = {ASE:.6f}")
+
+#-----------------------------------------------------------------------------------------------
+# Step 6: ASE under H0
+#-----------------------------------------------------------------------------------------------
+ASE0 = 2.0 / Dr * np.sqrt((CT * (C - D)**2).sum() - (P - Q)**2 / W)
+print(f"ASE0 = {ASE0:.6f}")
+
+#-----------------------------------------------------------------------------------------------
+# Step 7: 95% confidence interval
+#-----------------------------------------------------------------------------------------------
+print(f"\n95% CI: [{D_val - 1.96*ASE:.4f}, {D_val + 1.96*ASE:.4f}]")
+print(f"z-test (H0: D=0): z = {D_val / ASE0:.4f}")
+```
+
+Output:
+
+```
+CT:
+[[2. 2. 1.]
+ [1. 1. 3.]]
+W=10.0, r=[5. 5.]
+
+C (concordant counts):
+[[4. 3. 0.]
+ [0. 2. 4.]]
+D (discordant counts):
+[[0. 1. 2.]
+ [3. 1. 0.]]
+
+P=28.0, Q=8.0, Dr=50.0
+
+Somers' D = (P-Q)/Dr = 0.400000
+
+Row midranks R = [3. 8.]
+
+Residuals eps:
+[[  60.  -40. -240.]
+ [-190.   10.  160.]]
+
+ASE = 0.340353
+ASE0 = 0.314960
+
+95% CI: [-0.2671, 1.0671]
+z-test (H0: D=0): z = 1.2700
+```
+
+## 9. Production Implementation
+
+See `fastwoe.metrics.somersd_se()` - validated against the VUROCS R package with exact numerical agreement across binary, ordinal, and continuous targets.