fixing local breaks

YagmurGULEC · Jul 27, 2018 · 2005d02 · 2005d02
1 parent ac23beb
commit 2005d02
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Showing 2 changed files with 13 additions and 10 deletions.
diff --git a/fancyimpute/nuclear_norm_minimization.py b/fancyimpute/nuclear_norm_minimization.py
@@ -29,7 +29,7 @@ def __init__(
             min_value=None,
             max_value=None,
             error_tolerance=0.0001,
-            fast_but_approximate=True,
+            max_iters=50000,
             verbose=True):
         """
         Parameters
@@ -47,8 +47,8 @@ def __init__(
             Degree of error allowed on reconstructed values. If omitted then
             defaults to 0.0001
 
-        fast_but_approximate : bool
-            Use the faster but less accurate Splitting Cone Solver
+        max_iters : int
+            Maximum number of iterations for the convex solver
 
         verbose : bool
             Print debug info
@@ -59,7 +59,7 @@ def __init__(
             max_value=max_value)
         self.require_symmetric_solution = require_symmetric_solution
         self.error_tolerance = error_tolerance
-        self.fast_but_approximate = fast_but_approximate
+        self.max_iters = max_iters
         self.verbose = verbose
 
     def _constraints(self, X, missing_mask, S, error_tolerance):
@@ -119,6 +119,8 @@ def solve(self, X, missing_mask):
         problem = cvxpy.Problem(objective, constraints)
         problem.solve(
             verbose=self.verbose,
-            # SCS solver is known to be faster but less exact
-            solver=cvxpy.SCS if self.fast_but_approximate else None)
+            solver=cvxpy.SCS,
+            max_iters=self.max_iters,
+            # use_indirect, see: https://github.com/cvxgrp/cvxpy/issues/547
+            use_indirect=False)
         return S.value
diff --git a/test/test_nuclear_norm_minimization.py b/test/test_nuclear_norm_minimization.py
@@ -30,8 +30,9 @@ def create_rank1_data(symmetric=False):
 
 def test_rank1_convex_solver():
     XY_rank1, XY_missing_rank1 = create_rank1_data(symmetric=False)
-    XY_completed_rank1 = NuclearNormMinimization().complete(XY_missing_rank1)
-    assert abs(XY_completed_rank1[1, 2] - XY_rank1[1, 2]) < 0.001, \
+    solver = NuclearNormMinimization(max_iters=50000)
+    XY_completed_rank1 = solver.complete(XY_missing_rank1)
+    assert abs(XY_completed_rank1[1, 2] - XY_rank1[1, 2]) < 0.01, \
         "Expected %0.4f but got %0.4f" % (
             XY_rank1[1, 2], XY_completed_rank1[1, 2])
 
@@ -40,13 +41,13 @@ def test_rank1_symmetric_convex_solver():
     XYXY_rank1, XYXY_missing_rank1 = create_rank1_data(symmetric=True)
     solver = NuclearNormMinimization(require_symmetric_solution=True)
     completed = solver.complete(XYXY_missing_rank1)
-    assert abs(completed[1, 2] - XYXY_rank1[1, 2]) < 0.001, \
+    assert abs(completed[1, 2] - XYXY_rank1[1, 2]) < 0.01, \
         "Expected %0.4f but got %0.4f" % (
             XYXY_rank1[1, 2], completed[1, 2])
 
 
 def test_nuclear_norm_minimization_with_low_rank_random_matrix():
-    solver = NuclearNormMinimization(require_symmetric_solution=False)
+    solver = NuclearNormMinimization(max_iters=2000)
     XY_completed = solver.complete(XY_incomplete[:100])
     _, missing_mae = reconstruction_error(
         XY[:100], XY_completed, missing_mask[:100], name="NuclearNorm")