Merge pull request #4 from bcdev/heptaflar-test

Refactor Bernstein polynomial tests for clarity

Author: heptaflar

test/b/test_b_jax.py

@@ -92,7 +92,7 @@ def test_eval(self):
         c = self.c
         f = self.f
         y = f.eval(c)
-        precalculated = np.asarray(
+        y_precalculated = np.asarray(
             [
                 [
                     [19.8694, 19.7848, 20.3956],
@@ -111,8 +111,8 @@ def test_eval(self):
                 ],
             ]
         )
-        self.assertEqual((3, 3, 3), y.shape)
-        self.assertTrue(np.allclose(y, precalculated))
+        self.assertEqual(y_precalculated.shape, y.shape)
+        self.assertTrue(np.allclose(y, y_precalculated))
 
         g = f.jac(c)
         self.assertEqual(y.shape + c.shape, g.shape)
@@ -179,9 +179,9 @@ def test_bernstein_poly(self):
         )
         f = BernsteinPoly(c)
         y = f.eval(c, x)
-        precalculated = np.asarray([19.8694, 32.0761, 19.6774])
-        self.assertEqual((3,), y.shape)
-        self.assertTrue(jnp.allclose(y, precalculated))
+        y_precalculated = np.asarray([19.8694, 32.0761, 19.6774])
+        self.assertEqual(y_precalculated.shape, y.shape)
+        self.assertTrue(np.allclose(y, y_precalculated))
 
         g = f.jac_p(c, x)
         self.assertEqual((3,) + d, g.shape)
@@ -192,20 +192,20 @@ def test_bernstein_poly(self):
         self.assertTrue(np.all(g > 0.0))
 
     def test_from_lookup_table(self):
-        k = 5
-        x = np.asarray([0.00, 0.20, 0.40, 0.60, 0.80, 1.00])
-        y = np.square(x) + 2.0 * x + 3.0
-
-        f = BernsteinPoly.from_lookup_table((k,), (x,), y, non_negative=True)
-        c = f.prior()
-        self.assertEqual((k + 1,), c.shape)
-        self.assertAlmostEqual(3.0, c[0])
-        self.assertAlmostEqual(3.4, c[1])
-        self.assertAlmostEqual(3.9, c[2])
-        self.assertAlmostEqual(4.5, c[3])
-        self.assertAlmostEqual(5.2, c[4])
-        self.assertAlmostEqual(6.0, c[5])
-        self.assertTrue(jnp.allclose(f.eval(c, x), y))
+        k = (3, 4, 2)
+        d = tuple([k_ + 1 for k_ in k])
+        c = np.arange(np.prod(np.asarray(d))).reshape(d) + 1.0
+        x = (
+            np.asarray([0.0, 0.2, 0.4, 0.6, 0.8, 1.0]),
+            np.asarray([0.0, 0.2, 0.4, 0.6, 0.8, 1.0]),
+            np.asarray([0.0, 0.2, 0.4, 0.6, 0.8, 1.0]),
+        )
+        y = BernsteinGrid(x).eval(c)
+        
+        f = BernsteinPoly.from_lookup_table(k, x, y)
+        b = f.prior()
+        self.assertEqual(c.shape, b.shape)
+        self.assertTrue(np.allclose(b, c))
 
 
 class BSolveTest(unittest.TestCase):
@@ -217,49 +217,53 @@ class BSolveTest(unittest.TestCase):
     def test_b_solve_0_2(self):
         r"""Fit :math:`B_{0,2}(x)`."""
         k = 2
-        x = jnp.asarray([0.00, 0.20, 0.40, 0.60, 0.80, 1.00])
+        x = jnp.asarray([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
         y = jnp.square(1.0 - x)
 
         c = b_solve((k,), (x,), y, non_negative=True)
         self.assertEqual((k + 1,), c.shape)
+        self.assertFalse(np.any(c < 0.0))
         self.assertAlmostEqual(1.0, c[0].item())
         self.assertAlmostEqual(0.0, c[1].item())
         self.assertAlmostEqual(0.0, c[2].item())
 
     def test_b_solve_1_2(self):
         r"""Fit :math:`B_{1,2}(x)`."""
         k = 2
-        x = jnp.asarray([0.00, 0.20, 0.40, 0.60, 0.80, 1.00])
+        x = jnp.asarray([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
         y = 2.0 * x * (1.0 - x)
 
         c = b_solve((k,), (x,), y, non_negative=True)
         self.assertEqual((k + 1,), c.shape)
+        self.assertFalse(np.any(c < 0.0))
         self.assertAlmostEqual(0.0, c[0].item())
         self.assertAlmostEqual(1.0, c[1].item())
         self.assertAlmostEqual(0.0, c[2].item())
 
     def test_b_solve_2_2(self):
         r"""Fit :math:`B_{2,2}(x)`."""
         k = 2
-        x = jnp.asarray([0.00, 0.20, 0.40, 0.60, 0.80, 1.00])
+        x = jnp.asarray([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
         y = jnp.square(x)
 
         c = b_solve((k,), (x,), y, non_negative=True)
         self.assertEqual((k + 1,), c.shape)
+        self.assertFalse(np.any(c < 0.0))
         self.assertAlmostEqual(0.0, c[0].item())
         self.assertAlmostEqual(0.0, c[1].item())
         self.assertAlmostEqual(1.0, c[2].item())
 
     def test_b_solve_0_0_2_2(self):
         r"""Fit :math:`B_{(0,0),(2,2)}(x_0, x_1)`."""
         k = 2
-        x = jnp.asarray([0.00, 0.20, 0.40, 0.60, 0.80, 1.00])
+        x = jnp.asarray([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
         y = jnp.square(1.0 - x[jnp.newaxis, :]) * jnp.square(
             1.0 - x[:, jnp.newaxis]
         )
 
         c = b_solve((k, k), (x, x), y, non_negative=True)
         self.assertEqual((k + 1, k + 1), c.shape)
+        self.assertFalse(np.any(c < 0.0))
         self.assertAlmostEqual(1.0, c[0, 0].item())
         self.assertAlmostEqual(0.0, c[0, 1].item())
         self.assertAlmostEqual(0.0, c[0, 2].item())
@@ -273,7 +277,7 @@ def test_b_solve_0_0_2_2(self):
     def test_b_solve_1_0_2_2(self):
         r"""Fit :math:`B_{(1,0),(2,2)}(x_0, x_1)`."""
         k = 2
-        x = jnp.asarray([0.00, 0.20, 0.40, 0.60, 0.80, 1.00])
+        x = jnp.asarray([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
         y = (
             2.0
             * x[:, jnp.newaxis]
@@ -283,6 +287,7 @@ def test_b_solve_1_0_2_2(self):
 
         c = b_solve((k, k), (x, x), y, non_negative=True)
         self.assertEqual((k + 1, k + 1), c.shape)
+        self.assertFalse(np.any(c < 0.0))
         self.assertAlmostEqual(0.0, c[0, 0].item())
         self.assertAlmostEqual(0.0, c[0, 1].item())
         self.assertAlmostEqual(0.0, c[0, 2].item())
@@ -296,13 +301,14 @@ def test_b_solve_1_0_2_2(self):
     def test_b_solve_2_0_2_2(self):
         r"""Fit :math:`B_{(2,0),(2,2)}(x_0, x_1)`."""
         k = 2
-        x = jnp.asarray([0.00, 0.20, 0.40, 0.60, 0.80, 1.00])
+        x = jnp.asarray([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
         y = jnp.square(x[:, jnp.newaxis]) * jnp.square(
             1.0 - x[jnp.newaxis, :]
         )
 
         c = b_solve((k, k), (x, x), y, non_negative=True)
         self.assertEqual((k + 1, k + 1), c.shape)
+        self.assertFalse(np.any(c < 0.0))
         self.assertAlmostEqual(0.0, c[0, 0].item())
         self.assertAlmostEqual(0.0, c[0, 1].item())
         self.assertAlmostEqual(0.0, c[0, 2].item())
@@ -316,7 +322,7 @@ def test_b_solve_2_0_2_2(self):
     def test_b_solve_0_1_2_2(self):
         r"""Fit :math:`B_{(0,1),(2,2)}(x_0, x_1)`."""
         k = 2
-        x = jnp.asarray([0.00, 0.20, 0.40, 0.60, 0.80, 1.00])
+        x = jnp.asarray([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
         y = (
             2.0
             * jnp.square(1.0 - x[:, jnp.newaxis])
@@ -326,6 +332,7 @@ def test_b_solve_0_1_2_2(self):
 
         c = b_solve((k, k), (x, x), y, non_negative=True)
         self.assertEqual((k + 1, k + 1), c.shape)
+        self.assertFalse(np.any(c < 0.0))
         self.assertAlmostEqual(0.0, c[0, 0].item())
         self.assertAlmostEqual(1.0, c[0, 1].item())
         self.assertAlmostEqual(0.0, c[0, 2].item())
@@ -339,7 +346,7 @@ def test_b_solve_0_1_2_2(self):
     def test_b_solve_1_1_2_2(self):
         r"""Fit :math:`B_{(1,1),(2,2)}(x_0, x_1)`."""
         k = 2
-        x = jnp.asarray([0.00, 0.20, 0.40, 0.60, 0.80, 1.00])
+        x = jnp.asarray([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
         y = (
             4.0
             * x[:, jnp.newaxis]
@@ -350,6 +357,7 @@ def test_b_solve_1_1_2_2(self):
 
         c = b_solve((k, k), (x, x), y, non_negative=True)
         self.assertEqual((k + 1, k + 1), c.shape)
+        self.assertFalse(np.any(c < 0.0))
         self.assertAlmostEqual(0.0, c[0, 0].item())
         self.assertAlmostEqual(0.0, c[0, 1].item())
         self.assertAlmostEqual(0.0, c[0, 2].item())
@@ -363,7 +371,7 @@ def test_b_solve_1_1_2_2(self):
     def test_b_solve_2_1_2_2(self):
         r"""Fit :math:`B_{(2,1),(2,2)}(x_0, x_1)`."""
         k = 2
-        x = jnp.asarray([0.00, 0.20, 0.40, 0.60, 0.80, 1.00])
+        x = jnp.asarray([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
         y = (
             2.0
             * jnp.square(x[:, jnp.newaxis])
@@ -372,6 +380,7 @@ def test_b_solve_2_1_2_2(self):
         )
 
         c = b_solve((k, k), (x, x), y, non_negative=True)
+        self.assertFalse(np.any(c < 0.0))
         self.assertEqual((k + 1, k + 1), c.shape)
         self.assertAlmostEqual(0.0, c[0, 0].item())
         self.assertAlmostEqual(0.0, c[0, 1].item())
@@ -386,12 +395,13 @@ def test_b_solve_2_1_2_2(self):
     def test_b_solve_0_2_2_2(self):
         r"""Fit :math:`B_{(0,2),(2,2)}(x_0, x_1)`."""
         k = 2
-        x = jnp.asarray([0.00, 0.20, 0.40, 0.60, 0.80, 1.00])
+        x = jnp.asarray([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
         y = jnp.square(1.0 - x[:, jnp.newaxis]) * jnp.square(
             x[jnp.newaxis, :]
         )
 
         c = b_solve((k, k), (x, x), y, non_negative=True)
+        self.assertFalse(np.any(c < 0.0))
         self.assertEqual((k + 1, k + 1), c.shape)
         self.assertAlmostEqual(0.0, c[0, 0].item())
         self.assertAlmostEqual(0.0, c[0, 1].item())
@@ -406,7 +416,7 @@ def test_b_solve_0_2_2_2(self):
     def test_b_solve_1_2_2_2(self):
         r"""Fit :math:`B_{(1,2),(2,2)}(x_0, x_1)`."""
         k = 2
-        x = jnp.asarray([0.00, 0.20, 0.40, 0.60, 0.80, 1.00])
+        x = jnp.asarray([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
         y = (
             2.0
             * x[:, jnp.newaxis]
@@ -416,6 +426,7 @@ def test_b_solve_1_2_2_2(self):
 
         c = b_solve((k, k), (x, x), y, non_negative=True)
         self.assertEqual((k + 1, k + 1), c.shape)
+        self.assertFalse(np.any(c < 0.0))
         self.assertAlmostEqual(0.0, c[0, 0].item())
         self.assertAlmostEqual(0.0, c[0, 1].item())
         self.assertAlmostEqual(0.0, c[0, 2].item())
@@ -429,11 +440,12 @@ def test_b_solve_1_2_2_2(self):
     def test_b_solve_2_2_2_2(self):
         r"""Fit :math:`B_{(2,2),(2,2)}(x_0, x_1)`."""
         k = 2
-        x = jnp.asarray([0.00, 0.20, 0.40, 0.60, 0.80, 1.00])
+        x = jnp.asarray([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
         y = jnp.square(x[:, jnp.newaxis]) * jnp.square(x[jnp.newaxis, :])
 
         c = b_solve((k, k), (x, x), y, non_negative=True)
         self.assertEqual((k + 1, k + 1), c.shape)
+        self.assertFalse(np.any(c < 0.0))
         self.assertAlmostEqual(0.0, c[0, 0].item())
         self.assertAlmostEqual(0.0, c[0, 1].item())
         self.assertAlmostEqual(0.0, c[0, 2].item())

uncertaintyx/b/jax.py

@@ -5,8 +5,8 @@
 from typing import Self
 
 import jax
-import jax.lax.linalg as jla
 import jax.numpy as jnp
+import jax.numpy.linalg as jli
 import numpy as np
 import optax
 import optimistix
@@ -186,44 +186,35 @@ def b_solve(
 
     N = len(k)  # noqa: N806
     bases = [b_basis(k[i], x[i]) for i in range(N)]
-    facts = [jla.qr(B.T, full_matrices=False) for B in bases]  # noqa: N806
-    Q = [_[0] for _ in facts]  # noqa: N806
-    R = [_[1] for _ in facts]  # noqa: N806
+    grams = [jnp.dot(B, B.T) for B in bases]  # noqa: N806
 
-    # compute the right hand side of the triangular equation
+    # compute the right hand side of the normal equation
     rhs = y
     for i in range(N):
-        rhs = jnp.tensordot(rhs, Q[i], axes=(0, 0))
-    # solve the triangular equation
+        B = bases[i]  # noqa: N806
+        rhs = jnp.tensordot(rhs, B, axes=(0, 1))
+    # solve the normal equation
     c_unconstrained = rhs
-    if N > 1:
-        for i in range(N):
-            solve = jax.vmap(
-                lambda a, b: jla.triangular_solve(a, b, left_side=True),
-                in_axes=(None, i),
-                out_axes=i,
-            )
-            c_unconstrained = solve(R[i], c_unconstrained)
-    else:
-        c_unconstrained = jla.triangular_solve(
-            R[0], c_unconstrained, left_side=True
+    for i in range(N):
+        G = grams[i]  # noqa: N806
+        c_unconstrained = jnp.tensordot(
+            c_unconstrained, jli.pinv(G), axes=(0, 1)
         )
 
     def hvp(c: Array):
         """The Hessian-vector product."""
         res = c
         for i in range(N):
-            res = jnp.tensordot(res, R[i], axes=(0, 1))
-        for i in range(N):
-            res = jnp.tensordot(res, R[i], axes=(0, 0))
+            G = grams[i]  # noqa: N806
+            res = jnp.tensordot(res, G, axes=(0, 1))
         return res
 
-    def nnls(c: Array, rhs: Array):
+    def nnls(c: Array):
         """
         Non-negative least-squares solver.
 
-        Applies a positive transformation and an L-BFGS
-        optimizer to ensure non-negativity.
+        Applies a positive transformation and an L-BFGS optimizer
+        to ensure non-negativity.
         """
 
         def forward(u: Array) -> Array:
@@ -250,10 +241,6 @@ def make_minimizer():
                 optax.lbfgs(), atol=atol, rtol=rtol, norm=optimistix.max_norm
             )
 
-        # compute the right hand side of the normal equation
-        for i in range(N):
-            rhs = jnp.tensordot(rhs, R[i], axes=(0, 0))
-
         u = inverse(jnp.abs(c) + jnp.finfo(c.dtype).eps)
         optimum = optimistix.minimise(
             misfit, make_minimizer(), u, max_steps=max_steps, throw=False
@@ -264,13 +251,7 @@ def make_minimizer():
     nnls_needed = jnp.logical_and(
         non_negative, jnp.any(c_unconstrained < 0.0)
     )
-    return jax.lax.cond(
-        nnls_needed,
-        nnls,
-        lambda c, _: c,
-        c_unconstrained,
-        rhs,
-    )
+    return jax.lax.cond(nnls_needed, nnls, lambda c: c, c_unconstrained)
 
 
 def _lower_bounds(
@@ -327,9 +308,10 @@ def __init__(
         :param a: The lower bounds of the grid coordinates.
         :param b: The upper bounds of the grid coordinates.
         """
+        N = len(x)  # noqa: : N806
         a = _lower_bounds(a, x)
         b = _upper_bounds(b, x)
-        x = tuple(jnp.asarray((x_ - a) / (b - a)) for x_ in x)
+        x_ = tuple(jnp.asarray((x[i] - a[i]) / (b[i] - a[i])) for i in range(N))
 
         def f(c: Array) -> Array:
             r"""
@@ -420,9 +402,10 @@ def from_lookup_table(
         :param rtol: The relative tolerance for terminating the solver.
         :param max_steps: The maximum number of steps the solver can take.
         """
+        N = len(k)  # noqa: : N806
         a = _lower_bounds(a, x)
         b = _upper_bounds(b, x)
-        x_ = tuple(jnp.asarray((x_ - a) / (b - a)) for x_ in x)
+        x_ = tuple(jnp.asarray((x[i] - a[i]) / (b[i] - a[i])) for i in range(N))
         y_ = jnp.asarray(y)
         c_ = b_solve(
             k,