ENH add snapml solver (#25)

.github/workflows/main.yml

@@ -46,6 +46,11 @@ jobs:
 
     - run: conda info
 
+    - name: Install OpenMP
+      if: matrix.os == 'macos-latest'
+      run: |
+        brew install libomp
+
     - name: Install benchopt and its dependencies
       run: |
         conda info
@@ -58,6 +63,9 @@ jobs:
 
     - name: Test
       run: |
+        export OMP_NUM_THREADS=1  # see issue 20
+
+
         benchopt test . --env-name bench_test_env -vl
         benchopt test . --env-name bench_test_env -vl --skip-install
 

datasets/news20.py

@@ -10,16 +10,16 @@ class Dataset(BaseDataset):
     name = "news20"
     is_sparse = True
 
-    install_cmd = 'conda'
-    requirements = ['pip:libsvmdata']
+    install_cmd = "conda"
+    requirements = ["pip:libsvmdata"]
 
     def __init__(self):
         self.X, self.y = None, None
 
     def get_data(self):
 
         if self.X is None:
-            self.X, self.y = fetch_libsvm('news20')
+            self.X, self.y = fetch_libsvm("news20.binary")
 
         data = dict(X=self.X, y=self.y)
 

solvers/snapml.py

@@ -0,0 +1,33 @@
+from benchopt import BaseSolver, safe_import_context
+
+with safe_import_context() as import_ctx:
+    from snapml import LogisticRegression
+    import numpy as np
+
+
+class Solver(BaseSolver):
+    name = "snapml"
+
+    install_cmd = "conda"
+    requirements = ["pip:snapml"]
+
+    def set_objective(self, X, y, lmbd):
+        self.X, self.y, self.lmbd = X, y, lmbd
+
+        self.clf = LogisticRegression(
+            fit_intercept=False,
+            regularizer=self.lmbd,
+            penalty="l2",
+            tol=1e-12,
+        )
+
+    def run(self, n_iter):
+        if n_iter == 0:
+            self.clf.coef_ = np.zeros(self.X.shape[1])
+            return
+
+        self.clf.max_iter = n_iter
+        self.clf.fit(self.X, self.y)
+
+    def get_result(self):
+        return self.clf.coef_.flatten()