Fix CI: ignore notebook-specific lint errors

notebooks/ConstrainedRiskBudgeting.py

@@ -59,7 +59,7 @@
 from pyrb import ConstrainedRiskBudgeting
 
 plt.style.use("tableau-colorblind10")
-plt.rcParams.update({"figure.autolayout": True,"axes.grid": True})
+plt.rcParams.update({"figure.autolayout": True, "axes.grid": True})
 
 # %%
 vol = np.array([0.05, 0.05, 0.07, 0.1, 0.15, 0.15, 0.15, 0.18])
@@ -80,7 +80,7 @@
 )
 
 cov = np.outer(vol, vol) * corr
-asset_labels = [f"A{i+1}" for i in range(len(vol))]
+asset_labels = [f"A{i + 1}" for i in range(len(vol))]
 
 # %%
 scenario_specs = [
@@ -99,7 +99,12 @@
     {
         "name": "Add relative allocation",
         "description": "Adds w1 - w2 + w5 - w6 ≥ -5% on top of the high-vol cap.",
-        "C": np.array([[0.0, 0.0, 0.0, 0.0, -1.0, -1.0, -1.0, -1.0],[1.0, -1.0, 0.0, 0.0, 1.0, -1.0, 0.0, 0.0]]),
+        "C": np.array(
+            [
+                [0.0, 0.0, 0.0, 0.0, -1.0, -1.0, -1.0, -1.0],
+                [1.0, -1.0, 0.0, 0.0, 1.0, -1.0, 0.0, 0.0],
+            ]
+        ),
         "d": np.array([-0.3, -0.05]),
     },
 ]
@@ -150,25 +155,25 @@ def solve_crb_scenario(cov_matrix, spec):
     [res["weights"] for res in scenario_results],
     columns=asset_labels,
     index=summary_df.index,
- )
+)
 
 marginal_df = pd.DataFrame(
     [res["marginal_rc"] for res in scenario_results],
     columns=asset_labels,
     index=summary_df.index,
- )
+)
 
 risk_contrib_df = pd.DataFrame(
     [res["risk_contrib"] for res in scenario_results],
     columns=asset_labels,
     index=summary_df.index,
- )
+)
 
 risk_contrib_pct_df = pd.DataFrame(
     [res["risk_contrib_pct"] for res in scenario_results],
     columns=asset_labels,
     index=summary_df.index,
- )
+)
 
 constraint_df = pd.DataFrame(
     [
@@ -178,15 +183,17 @@ def solve_crb_scenario(cov_matrix, spec):
         for res in scenario_results
     ],
     index=summary_df.index,
- )
+)
 
 summary_display = summary_df.copy()
 for col in ["Total risk (volatility)", "λ*", "Sum of weights"]:
     summary_display[col] = summary_display[col].astype(float).round(4)
 
+
 def format_df(df, formatter):
     return df.apply(lambda col: col.map(formatter))
 
+
 weights_display = format_df(weights_df, lambda x: f"{x:.2%}")
 risk_contrib_display = format_df(risk_contrib_df, lambda x: f"{x:.4f}")
 risk_contrib_pct_display = format_df(risk_contrib_pct_df, lambda x: f"{x:.2%}")
@@ -209,7 +216,9 @@ def format_df(df, formatter):
 
 if not constraint_df.empty:
     constraint_display = constraint_df.copy().astype(float).round(4)
-    constraint_display.columns = [f"Constraint {i+1}" for i in range(constraint_display.shape[1])]
+    constraint_display.columns = [
+        f"Constraint {i + 1}" for i in range(constraint_display.shape[1])
+    ]
     print("\nConstraint evaluations Cw")
     display(constraint_display)
 

pyproject.toml

@@ -63,6 +63,7 @@ ignore = [
 [tool.ruff.lint.per-file-ignores]
 "__init__.py" = ["F401"]
 "tests/**/*" = ["F401", "F811"]
+"notebooks/**/*" = ["F821", "B018"]  # Notebooks may use IPython display() and have useless expressions for display
 
 [tool.ruff.lint.isort]
 known-first-party = ["pyrb"]

src/pyrb/solvers.py

@@ -38,7 +38,9 @@ def accelerate(_varphi, r, s, u, alpha=10, tau=2):
     "Tuple((float64[:], float64[:], float64))(float64[:], float64, float64[:], float64, float64, float64[:], float64[:], float64[:], float64[:,:], float64, float64[:,:])",
     nopython=True,
 )
-def _cycle(x, c, var, _varphi, sigma_x, Sx, budgets, expected_returns, bounds, lambda_log, cov):
+def _cycle(
+    x, c, var, _varphi, sigma_x, Sx, budgets, expected_returns, bounds, lambda_log, cov
+):
     """
     Internal numba function for computing one cycle of the CCD algorithm.
 
@@ -59,7 +61,13 @@ def _cycle(x, c, var, _varphi, sigma_x, Sx, budgets, expected_returns, bounds, l
 
 
 def solve_rb_ccd(
-    cov, budgets=None, expected_returns=None, risk_aversion=1.0, bounds=None, lambda_log=1.0, _varphi=0.0
+    cov,
+    budgets=None,
+    expected_returns=None,
+    risk_aversion=1.0,
+    bounds=None,
+    lambda_log=1.0,
+    _varphi=0.0,
 ):
     """Solve the risk budgeting problem using cyclical coordinate descent.
 
@@ -122,7 +130,17 @@ def solve_rb_ccd(
 
     while not cvg:
         x, sx, sigma_x = _cycle(
-            x, risk_aversion, var, _varphi, sigma_x, sx, budgets, expected_returns, bounds, lambda_log, cov
+            x,
+            risk_aversion,
+            var,
+            _varphi,
+            sigma_x,
+            sx,
+            budgets,
+            expected_returns,
+            bounds,
+            lambda_log,
+            cov,
         )
         cvg = np.sum(np.array(x - x0) ** 2) <= CCD_CONVERGENCE_TOL
         x0 = x.copy()

tests/test_risk_budgeting.py

@@ -62,6 +62,7 @@ def test_cerb():
     np.testing.assert_almost_equal(CRB.get_risk_contributions()[1], 0.2455, decimal=5)
     np.testing.assert_almost_equal(np.sum(CRB.weights[1]), 0.2)
 
+
 def test_rb_with_equal_budgets():
     equal_budgets = [1.0 / NUMBER_OF_ASSET] * NUMBER_OF_ASSET
     RB = RiskBudgeting(COVARIANCE_MATRIX, equal_budgets)
@@ -82,7 +83,12 @@ def test_cerb_with_expected_returns():
     d = [-0.3]
 
     CRB = ConstrainedRiskBudgeting(
-        COVARIANCE_MATRIX, budgets=RISK_BUDGETS, expected_returns=EXPECTED_RETURNS, bounds=BOUNDS, C=C, d=d
+        COVARIANCE_MATRIX,
+        budgets=RISK_BUDGETS,
+        expected_returns=EXPECTED_RETURNS,
+        bounds=BOUNDS,
+        C=C,
+        d=d,
     )
     CRB.solve()