Merge branch 'develop' into updating-residuals

washingtonpost · Sep 24, 2024 · 585ae38 · 585ae38
2 parents 64debde + e551069
commit 585ae38
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -1,4 +1,7 @@
 # Changelog
+### 2.1.1 - 2024-09-12
+- chore: Updated all required packages to their latest versions.  Switched `QuantileRegressionSolver`'s solver from `ECOS` to `Clarabel` [#22](https://github.com/washingtonpost/elex-solver/pull/22)	
+
 ### 2.1.0 - 2024-03-29
 - feat: Transition matrix solver. [#17](https://github.com/washingtonpost/elex-solver/pull/17)
 

diff --git a/setup.py b/setup.py
@@ -3,7 +3,7 @@
 
 from setuptools import find_packages, setup
 
-INSTALL_REQUIRES = ["cvxpy~=1.4", "numpy~=1.26", "scipy~=1.12"]
+INSTALL_REQUIRES = ["cvxpy~=1.5", "numpy~=2.1", "scipy~=1.14"]
 
 THIS_FILE_DIR = os.path.dirname(__file__)
 
@@ -13,7 +13,7 @@
     LONG_DESCRIPTION = f.read()
 
 # The full version, including alpha/beta/rc tags
-RELEASE = "2.1.0"
+RELEASE = "2.1.1"
 # The short X.Y version
 VERSION = ".".join(RELEASE.split(".")[:2])
 

diff --git a/src/elexsolver/QuantileRegressionSolver.py b/src/elexsolver/QuantileRegressionSolver.py
@@ -66,15 +66,15 @@ def _fit_with_regularization(
         Fits quantile regression with regularization
         TODO: convert this problem to use the dual like in the non regularization case
         """
-        arguments = {"ECOS": {"max_iters": 10000}}
+        arguments = {cp.CLARABEL: {"max_iter": 10000}}
         coefficients = cp.Variable((x.shape[1],))
         y_hat = x @ coefficients
         residual = y - y_hat
         loss_function = cp.sum(cp.multiply(weights, 0.5 * cp.abs(residual) + (tau - 0.5) * residual))
         loss_function += lambda_ * self._get_regularizer(coefficients, regularize_intercept, n_feat_ignore_reg)
         objective = cp.Minimize(loss_function)
         problem = cp.Problem(objective)
-        problem.solve(solver="ECOS", **arguments.get("ECOS", {}))
+        problem.solve(solver=cp.CLARABEL, **arguments.get(cp.CLARABEL, {}))
         return coefficients.value
 
     def fit(