ENH add support for ncvreg

solvers/ncvreg.py

@@ -0,0 +1,75 @@
+from benchopt import BaseSolver, safe_import_context
+
+with safe_import_context() as import_ctx:
+    import numpy as np
+    from benchopt.helpers.r_lang import import_rpackages
+    from rpy2 import robjects
+    from rpy2.robjects import numpy2ri
+    from scipy import sparse
+
+    # Setup the system to allow rpy2 running
+    numpy2ri.activate()
+    import_rpackages("ncvreg")
+
+
+class Solver(BaseSolver):
+    name = "ncvreg"
+
+    install_cmd = "conda"
+    requirements = ["r-base", "rpy2", "r-ncvreg"]
+    references = [
+        'P. Breheny and J. Huang, "Coordinate descent algorithms for nonconvex'
+        "penalized regression, with applications to biological feature"
+        'selection," The Annals of Applied Statistics, vol. 5, no. 1, pp.'
+        "232–253, Mar. 2011, doi: 10.1214/10-AOAS388."
+    ]
+
+    def set_objective(self, X, y, lmbd, fit_intercept):
+        self.lmbd = lmbd
+        self.fit_intercept = fit_intercept
+        self.n, self.p = X.shape
+
+        penalty_factor = np.ones(self.p)
+
+        # NOTE(jolars): To fit the intercept, we add a column of ones at the
+        # end of X and set its penalty factor to zero.
+        self.X = X
+
+        if self.fit_intercept:
+            X = np.hstack((X, np.ones((self.n, 1))))
+            penalty_factor = np.hstack((penalty_factor, np.array([0.0])))
+
+        self.penalty_factor = robjects.vectors.FloatVector(penalty_factor)
+
+        self.Xmod = robjects.r.matrix(X, X.shape[0], X.shape[1])
+        self.y = robjects.vectors.FloatVector(y)
+
+        # Standardization of X cannot be turned off in standard interface to
+        # ncvreg, so we have to use ncvfit directly instead
+        self.ncvfit = robjects.r["ncvfit"]
+
+    def skip(self, X, y, lmbd, fit_intercept):
+        if sparse.issparse(X):
+            return True, "ncvreg does not support sparse X"
+
+        return False, None
+
+    def run(self, n_iter):
+        fit_dict = {
+            "max.iter": n_iter,
+            "lambda": self.lmbd / self.n,
+            "penalty.factor": self.penalty_factor,
+        }
+
+        self.fit = self.ncvfit(
+            self.Xmod,
+            self.y,
+            penalty="lasso",
+            eps=1e-15,
+            **fit_dict
+        )
+
+    def get_result(self):
+        results = dict(zip(self.fit.names, list(self.fit)))
+
+        return results["beta"]