def test_rank1_symmetric_convex_solver():
XYXY_rank1, XYXY_missing_rank1 = create_rank1_data(symmetric=True)
solver = NuclearNormMinimization(require_symmetric_solution=True)
completed = solver.fit_transform(XYXY_missing_rank1)
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_rank1_convex_solver():
XY_rank1, XY_missing_rank1 = create_rank1_data(symmetric=False)
solver = NuclearNormMinimization(max_iters=50000)
XY_completed_rank1 = solver.fit_transform(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])
def test_nuclear_norm_minimization_with_low_rank_random_matrix():
solver = NuclearNormMinimization(max_iters=2000)
XY_completed = solver.fit_transform(XY_incomplete[:100])
_, missing_mae = reconstruction_error(XY[:100],
XY_completed,
missing_mask[:100],
name="NuclearNorm")
assert missing_mae < 0.1, "Error too high!"