ENH: Add n_iter_ attribute to Survival SVM models

Fixes #277

sksurv/svm/minlip.py

@@ -58,7 +58,8 @@ def solve(self, P, q, G, h):
             # non of solved, solved inaccurate
             raise RuntimeError("OSQP solver failed: {}".format(results.info.status))
 
-        return results.x[numpy.newaxis]
+        n_iter = results.info.iter
+        return results.x[numpy.newaxis], n_iter
 
     def _get_options(self):
         solver_opts = {
@@ -134,7 +135,8 @@ def solve(self, P, q, G, h):
 
         # drop solution for t
         x = results["x"][1:]
-        return x[numpy.newaxis]
+        n_iter = results["info"]["iter"]
+        return x[numpy.newaxis], n_iter
 
     def _check_success(self, results):  # pylint: disable=no-self-use
         exit_flag = results["info"]["exitFlag"]
@@ -259,6 +261,9 @@ class MinlipSurvivalAnalysis(BaseEstimator, SurvivalAnalysisMixin):
         Names of features seen during ``fit``. Defined only when `X`
         has feature names that are all strings.
 
+    n_iter_ : int
+        Number of iterations run by the optimization routine to fit the model.
+
     References
     ----------
     .. [1] Van Belle, V., Pelckmans, K., Suykens, J. A. K., and Van Huffel, S.
@@ -342,8 +347,9 @@ def _inner():
             timer = timeit.Timer(_inner)
             self.timings_ = timer.repeat(self.timeit, number=1)
 
-        coef = solver.solve(**problem_data)
+        coef, n_iter = solver.solve(**problem_data)
         self._update_coef(coef, D)
+        self.n_iter_ = n_iter
         self.X_fit_ = x
 
     def _update_coef(self, coef, D):
@@ -480,6 +486,9 @@ class HingeLossSurvivalSVM(MinlipSurvivalAnalysis):
         Names of features seen during ``fit``. Defined only when `X`
         has feature names that are all strings.
 
+    n_iter_ : int
+        Number of iterations run by the optimization routine to fit the model.
+
     References
     ----------
     .. [1] Van Belle, V., Pelckmans, K., Suykens, J. A., & Van Huffel, S.

sksurv/svm/naive_survival_svm.py

@@ -81,6 +81,11 @@ class NaiveSurvivalSVM(SurvivalAnalysisMixin, LinearSVC):
     max_iter : int, default: 1000
         The maximum number of iterations to be run.
 
+    Attributes
+    ----------
+    n_iter_ : int
+        Number of iterations run by the optimization routine to fit the model.
+
     See also
     --------
     sksurv.svm.FastSurvivalSVM

sksurv/svm/survival_svm.py

@@ -726,6 +726,10 @@ def fit(self, X, y):
 
         return self
 
+    @property
+    def n_iter_(self):
+        return self.optimizer_result_.nit
+
     @staticmethod
     def _argsort_and_resolve_ties(time, random_state):
         """Like numpy.argsort, but resolves ties uniformly at random"""
@@ -832,6 +836,9 @@ class FastSurvivalSVM(BaseSurvivalSVM, SurvivalAnalysisMixin):
         Names of features seen during ``fit``. Defined only when `X`
         has feature names that are all strings.
 
+    n_iter_ : int
+        Number of iterations run by the optimization routine to fit the model.
+
     See also
     --------
     FastKernelSurvivalSVM
@@ -971,6 +978,9 @@ class FastKernelSurvivalSVM(BaseSurvivalSVM, SurvivalAnalysisMixin):
         Names of features seen during ``fit``. Defined only when `X`
         has feature names that are all strings.
 
+    n_iter_ : int
+        Number of iterations run by the optimization routine to fit the model.
+
     See also
     --------
     FastSurvivalSVM

tests/test_minlip.py

@@ -245,6 +245,7 @@ def test_toy_minlip_fit_osqp(self, toy_data):
         m.set_params(alpha=2)
         m.fit(x, y)
 
+        assert m.n_iter_ > 100
         assert (1, x.shape[0]) == m.coef_.shape
         assert 1 == m.coef0
         expected_coef = numpy.array([
@@ -355,6 +356,7 @@ def test_toy_minlip_fit_ecos(self, toy_data):
         m.set_params(alpha=2)
         m.fit(x, y)
 
+        assert m.n_iter_ > 10
         assert (1, x.shape[0]) == m.coef_.shape
         assert 1 == m.coef0
         expected_coef = numpy.array([
@@ -463,6 +465,8 @@ def test_breast_cancer_osqp(gbsg2):
 
         assert (1, x.shape[0]) == m.coef_.shape
 
+        assert m.n_iter_ > 1000
+
         p = m.predict(x)
         assert_cindex_almost_equal(y['cens'], y['time'], p,
                                    (0.5990741854033906, 79720, 53352, 0, 42))
@@ -527,6 +531,8 @@ def test_breast_cancer_ecos(gbsg2):
 
         assert (1, x.shape[0]) == m.coef_.shape
 
+        assert m.n_iter_ > 10
+
         p = m.predict(x)
         assert_cindex_almost_equal(y['cens'], y['time'], p,
                                    (0.5990741854033906, 79720, 53352, 0, 42))

tests/test_survival_svm.py

@@ -282,6 +282,7 @@ def test_default_optimizer(make_whas500):
             warnings.simplefilter("ignore", category=ConvergenceWarning)
             ssvm.fit(whas500.x, whas500.y)
         assert 'avltree' == ssvm.optimizer
+        assert 1 < ssvm.n_iter_ <= 25
 
     @staticmethod
     @pytest.mark.slow()
@@ -405,6 +406,7 @@ def test_default_optimizer(make_whas500):
             warnings.simplefilter("ignore", category=ConvergenceWarning)
             ssvm.fit(whas500.x, whas500.y)
         assert 'rbtree' == ssvm.optimizer
+        assert 1 < ssvm.n_iter_ <= 25
 
     @staticmethod
     def test_unknown_optimizer(fake_data):
@@ -776,6 +778,8 @@ def test_survival_squared_hinge_loss(whas500_without_ties):
         nrsvm = NaiveSurvivalSVM(loss='squared_hinge', dual=False, tol=8e-7, max_iter=1000, random_state=0)
         nrsvm.fit(x, y)
 
+        assert nrsvm.n_iter_ > 10
+
         rsvm = FastSurvivalSVM(optimizer='avltree', tol=8e-7, max_iter=1000, random_state=0)
         rsvm.fit(x, y)