FIX adapt the code to new API with literal requirements

.github/workflows/main.yml

@@ -11,8 +11,8 @@ on:
     branches:
       - main
   schedule:
-    # Run every day at 7:42am UTC.
-    - cron:  '42 7 * * *'
+    # Run every 3rd of the month at 7:42am UTC.
+    - cron:  '42 7 3 * *'
 
 jobs:
   benchopt_dev:

datasets/finance.py

@@ -16,7 +16,7 @@ class Dataset(BaseDataset):
     is_sparse = True
 
     install_cmd = 'conda'
-    requirements = ['pip:libsvmdata']
+    requirements = ['pip::libsvmdata']
 
     def __init__(self):
         self.X, self.y = None, None

datasets/libsvm.py

@@ -17,7 +17,7 @@ class Dataset(BaseDataset):
     }
 
     install_cmd = 'conda'
-    requirements = ['pip:git+https://github.com/mathurinm/libsvmdata@main']
+    requirements = ['pip::git+https://github.com/mathurinm/libsvmdata@main']
     references = [
         "C. Chang and CJ. Lin, "
         "'ACM transactions on intelligent systems and technology (TIST)', "

objective.py

@@ -8,7 +8,7 @@ class Objective(BaseObjective):
     """L1 regularized linear regression.
     """
     name = "Lasso Regression"
-    min_benchopt_version = "1.5"
+    min_benchopt_version = "1.7"
 
     parameters = {
         'fit_intercept': [True, False],

solvers/blitz.py

@@ -16,7 +16,7 @@ class Solver(BaseSolver):
 
     install_cmd = 'conda'
     requirements = [
-        'pip:git+https://github.com/tbjohns/blitzl1.git@master'
+        'pip::git+https://github.com/tbjohns/blitzl1.git@master'
     ]
 
     def set_objective(self, X, y, lmbd, fit_intercept):

solvers/celer.py

@@ -18,7 +18,7 @@ class Solver(BaseSolver):
     sampling_strategy = 'iteration'
 
     install_cmd = 'conda'
-    requirements = ['pip:celer']
+    requirements = ['pip::celer']
 
     def set_objective(self, X, y, lmbd, fit_intercept):
         self.X, self.y, self.lmbd = X, y, lmbd

solvers/cuml.py

@@ -25,10 +25,10 @@ class Solver(BaseSolver):
 
     install_cmd = "conda"
     requirements = [
-        "rapidsai:rapids",
-        f"nvidia:cudatoolkit={cuda_version}",
+        "rapidsai::rapids",
+        "nvidia::'cuda-version>=11.4,<=12.8'",
         "dask-sql", "cupy"
-    ] if cuda_version is not None else []
+    ]
 
     parameters = {
         "solver": [

solvers/cyanure.py

@@ -12,7 +12,7 @@ class Solver(BaseSolver):
     name = 'Cyanure'
 
     install_cmd = 'conda'
-    requirements = ['mkl', 'pip:cyanure-mkl']
+    requirements = ['mkl', 'pip::cyanure-mkl']
     references = [
         'J. Mairal, "Cyanure: An Open-Source Toolbox for Empirical Risk'
         ' Minimization for Python, C++, and soon more," '

solvers/lars.py

@@ -28,8 +28,7 @@ def set_objective(self, X, y, lmbd, fit_intercept):
         n_samples = self.X.shape[0]
 
         self.clf = LassoLars(
-            alpha=self.lmbd / n_samples, fit_intercept=fit_intercept,
-            normalize=False,
+            alpha=self.lmbd / n_samples, fit_intercept=fit_intercept
         )
 
         warnings.filterwarnings("ignore", category=ConvergenceWarning)

solvers/modopt_fista.py

@@ -16,9 +16,7 @@ class Solver(BaseSolver):
     name = 'ModOpt-FISTA'
     sampling_strategy = 'callback'
     install_cmd = 'conda'
-    requirements = [
-        'pip:modopt',
-    ]
+    requirements = ['pip::modopt']
     parameters = {
         'restart_strategy': ['greedy', 'adaptive-1'],
     }

solvers/modopt_pogm.py

@@ -17,9 +17,7 @@ class Solver(BaseSolver):
     sampling_strategy = 'callback'
 
     install_cmd = 'conda'
-    requirements = [
-        'pip:modopt',
-    ]
+    requirements = ['pip::modopt']
     references = [
         'S. Farrens, A. Grigis, L. El Gueddari, Z. Ramzi, G. R. Chaithya, '
         'S. Starck, B. Sarthou, H. Cherkaoui, P. Ciuciu and J.-L. Starck, '

solvers/skglm.py

@@ -18,7 +18,7 @@ class Solver(BaseSolver):
 
     install_cmd = 'conda'
     requirements = [
-        'pip:skglm'
+        'pip::git+https://github.com/scikit-learn-contrib/skglm.git'
     ]
 
     def set_objective(self, X, y, lmbd, fit_intercept):

solvers/snapml.py

@@ -11,7 +11,7 @@ class Solver(BaseSolver):
     name = "snapml"
 
     install_cmd = "conda"
-    requirements = ["pip:snapml"]
+    requirements = ["pip::snapml"]
 
     stopping_criterion = SufficientDescentCriterion(eps=1e-10, patience=7)
 

test_config.py

@@ -18,6 +18,10 @@ def check_test_solver_install(solver_class):
     if "spams" in solver_class.name.lower():
         pytest.skip("python-spams is not released for python 3.9 yet")
 
+    if "snapml" in solver_class.name.lower():
+        if sys.platform == "darwin":
+            pytest.xfail("SnapML is not supported on MacOS.")
+
     if "cuml" in solver_class.name.lower():
         if sys.platform == "darwin":
             pytest.xfail("Cuml is not supported on MacOS.")