Add lagh: certified symbolic law discovery

.github/workflows/ci.yml

@@ -28,6 +28,9 @@ on:
 
 env: 
   CACHE_NUMBER: 0
+  # the 2022 baseline env pulls the deprecated PyPI "sklearn" shim, which now
+  # hard-errors on install; this is the workaround the error message names
+  SKLEARN_ALLOW_DEPRECATED_SKLEARN_PACKAGE_INSTALL: "True"
 
 jobs:
   ################################################################################
@@ -41,18 +44,18 @@ jobs:
     steps:
       - 
         name: Checkout code
-        uses: actions/checkout@v2
+        uses: actions/checkout@v4
       - 
-        name: Setup Mambaforge
-        uses: conda-incubator/setup-miniconda@v2
+        name: Setup Miniforge
+        uses: conda-incubator/setup-miniconda@v3
         with:
-          miniforge-variant: Mambaforge
+          miniforge-variant: Miniforge3
           miniforge-version: latest
           activate-environment: srbench
           use-mamba: true
       - 
         name: Cache conda
-        uses: actions/cache@v2
+        uses: actions/cache@v4
         with:
           path: /usr/share/miniconda3/envs/srbench
           key: ${{ runner.os }}-conda-${{ env.CACHE_NUMBER }}-${{hashFiles('environment.yml','experiment/methods/src/*.sh')}}-${{ github.sha }}
@@ -85,7 +88,7 @@ jobs:
     steps:
       - 
         name: Checkout code
-        uses: actions/checkout@v2
+        uses: actions/checkout@v4
       - 
         name: generate alg list
         run: bash ci/get_algorithm_list.sh
@@ -111,18 +114,18 @@ jobs:
     steps:
       - 
         name: Checkout code
-        uses: actions/checkout@v2
+        uses: actions/checkout@v4
       - 
-        name: Setup Mambaforge
-        uses: conda-incubator/setup-miniconda@v2
+        name: Setup Miniforge
+        uses: conda-incubator/setup-miniconda@v3
         with:
-          miniforge-variant: Mambaforge
+          miniforge-variant: Miniforge3
           miniforge-version: latest
           activate-environment: srbench
           use-mamba: true
       - 
         name: Cache conda
-        uses: actions/cache@v2
+        uses: actions/cache@v4
         with:
           path: /usr/share/miniconda3/envs/srbench
           key: ${{ runner.os }}-conda-${{ env.CACHE_NUMBER }}-${{ hashFiles('environment.yml','experiment/methods/src/*.sh') }}-${{ github.sha }}
@@ -156,18 +159,18 @@ jobs:
     steps:
       - 
         name: Checkout code
-        uses: actions/checkout@v2
+        uses: actions/checkout@v4
       - 
-        name: Setup Mambaforge
-        uses: conda-incubator/setup-miniconda@v2
+        name: Setup Miniforge
+        uses: conda-incubator/setup-miniconda@v3
         with:
-          miniforge-variant: Mambaforge
+          miniforge-variant: Miniforge3
           miniforge-version: latest
           activate-environment: srbench
           use-mamba: true
       - 
         name: Cache conda
-        uses: actions/cache@v2
+        uses: actions/cache@v4
         with:
           path: /usr/share/miniconda3/envs/srbench
           key: ${{ runner.os }}-conda-${{ env.CACHE_NUMBER }}-${{ hashFiles('environment.yml','experiment/methods/src/*.sh') }}-${{ github.sha }}

algorithms/lagh/install.sh

@@ -0,0 +1,4 @@
+#!/usr/bin/env bash
+# SRBench install hook: pull lagh from its stable source repository.
+set -e
+pip install "git+https://github.com/jascal/lagh@master"

algorithms/lagh/metadata.yml

@@ -0,0 +1,15 @@
+# SRBench method metadata (contract: algorithms/feat/metadata.yml format)
+name: lagh
+short_name: lagh
+description: >
+  Certified symbolic law discovery. Outputs are three-track: machine-checked
+  exact certificates (every point within a declared epsilon; chance-fit
+  significance bound alpha reported), labeled empirical conjectures, or
+  explicit abstentions. For this harness the conjecture track always answers;
+  certificate status is carried as estimator metadata (track_/tag_/
+  alpha_log10_). Zero-confident-wrong invariant: the system never claims an
+  exact law it cannot certify.
+authors: James Allan Scott (jascal)
+url: https://github.com/jascal/lagh
+license: Apache-2.0
+language: python

algorithms/lagh/regressor.py

@@ -0,0 +1,39 @@
+"""SRBench entry for lagh (certified symbolic law discovery).
+
+Contract objects: `est` (sklearn-compatible regressor), `model(est, X)`
+(sympy-compatible string), `eval_kwargs`.
+"""
+from lagh.sklearn import LaghRegressor
+
+est = LaghRegressor(max_time=3600)
+
+
+def model(est, X=None):
+    """Sympy-compatible model string, with x_i mapped to X's column names
+    (SRBench requirement: variable names must match the training DataFrame)."""
+    m = est.model()
+    if X is not None and hasattr(X, "columns"):
+        mapping = {"x_" + str(i): k for i, k in enumerate(X.columns)}
+        for k, v in reversed(list(mapping.items())):
+            m = m.replace(k, str(v))
+    return m
+
+
+eval_kwargs = {}
+
+
+def complexity(est):
+    """Parse-tree node count of the final expression (srbench_2025 fallback;
+    normally computed from the sympy string)."""
+    import sympy as sp
+    e = sp.sympify(est.model())
+    return int(sp.count_ops(e, visual=False)) + len(e.free_symbols) + 1
+
+
+def get_population(est):
+    """lagh keeps a single certified/conjectured model, not a population."""
+    return [est]
+
+
+def get_best_solution(est):
+    return est

algorithms/lagh/requirements.txt

@@ -0,0 +1,2 @@
+numpy
+sympy