lstsq test coverage

Author: shanemcq18

tests/lstsq/test_base.py

@@ -314,6 +314,8 @@ def test_fit(self):
         out = solver.fit(D, Z)
         assert out is solver
 
+        repr(solver)
+
     def test_predict(self, k=20, d=11, r=3):
         """Test predict()."""
         # Set up and manually solve a least-squares problem.

tests/lstsq/test_tikhonov.py

@@ -87,7 +87,9 @@ def test_fit(self, k=20, d=11, r=3):
         solver = self.Solver(1)
         A = np.random.standard_normal((k, d))
         B = np.random.standard_normal((r, k))
+        solver.verify()
         solver.fit(A, B)
+        solver.verify()
 
         for attr, shape in [
             ("_ZPhi", (r, d)),
@@ -101,6 +103,8 @@ def test_fit(self, k=20, d=11, r=3):
             assert isinstance(obj, np.ndarray)
             assert obj.shape == shape
 
+        repr(solver)
+
     def test_predict(self, k=20, d=10, r=5):
         """Test predict()."""
         solver1D = self.Solver(0)
@@ -346,6 +350,8 @@ def test_regularizer(self, k=10, d=6, r=3):
         solver.fit(A, B)
         assert solver.r == r
 
+        repr(solver)
+
     # Main methods ------------------------------------------------------------
     def test_predict(self, k=20, d=10):
         """Test predict()."""
@@ -551,13 +557,20 @@ def test_regularizer(self, k=20, d=11, r=3):
         assert solver.regularizer.shape == (d, d)
         assert np.all(solver.regularizer == 2 * np.eye(d))
 
+        repr(solver)
+        solver.method = "normal"
+        repr(solver)
+
     # Main methods ------------------------------------------------------------
     def test_fit(self, k=20, d=10, r=5):
         """Test fit()."""
         Z = np.zeros((d, d))
         A = np.random.standard_normal((k, d))
         B = np.random.standard_normal((r, k))
-        solver = self.Solver(Z).fit(A, B)
+        solver = self.Solver(Z)
+        solver.verify(d=d, r=r)
+        solver.fit(A, B)
+        solver.verify()
 
         for attr, shape in [
             ("data_matrix", (k, d)),
@@ -826,6 +839,8 @@ def test_regularizer(self, k=10, d=6, r=3):
         solver.regularizer = [[i] * d for i in range(1, r + 1)]
         assert np.all(solver.regularizer[0] == np.eye(d))
 
+        repr(solver)
+
     # Main methods ------------------------------------------------------------
     def test_predict(self, k=20, d=10):
         """Test lstsq._tikhonov.TikhonovDecoupledSolver.predict()."""

tests/lstsq/test_total.py

@@ -18,8 +18,13 @@ def test_fit(self, k=20, d=10, r=6):
         D = np.random.standard_normal((k, d))
         Z = np.random.random((r, k))
         solver = self.Solver()
+        solver.verify()
         out = solver.fit(D, Z)
         assert out is solver
+        repr(solver)
+        assert isinstance(solver.augcond, float)
+        assert isinstance(solver.error, float)
+        solver.verify()
 
     def test_predict(self, k=15, d=7, r=5):
         """Test predict()."""