Move linalg rewrites, delete sandbox

pytensor/sandbox/linalg/__init__.py

-from pytensor.sandbox.linalg.ops import spectral_radius_bound

pytensor/sandbox/minimal.py

-import numpy as np
-
-from pytensor.graph.basic import Apply
-from pytensor.graph.op import Op
-from pytensor.tensor.type import lscalar
-
-
-class Minimal(Op):
-    # TODO : need description for class
-
-    # if the Op has any attributes, consider using them in the eq function.
-    # If two Apply nodes have the same inputs and the ops compare equal...
-    # then they will be MERGED so they had better have computed the same thing!
-
-    __props__ = ()
-
-    def __init__(self):
-        # If you put things here, think about whether they change the outputs
-        # computed by # self.perform()
-        #  - If they do, then you should take them into consideration in
-        #    __eq__ and __hash__
-        #  - If they do not, then you should not use them in
-        #    __eq__ and __hash__
-
-        super().__init__()
-
-    def make_node(self, *args):
-        # HERE `args` must be PYTENSOR VARIABLES
-        return Apply(op=self, inputs=args, outputs=[lscalar()])
-
-    def perform(self, node, inputs, out_):
-        (output,) = out_
-        # HERE `inputs` are PYTHON OBJECTS
-
-        # do what you want here,
-        # but do not modify any of the arguments [inplace].
-        print("perform got %i arguments" % len(inputs))
-
-        print("Max of input[0] is ", np.max(inputs[0]))
-
-        # return some computed value.
-        # do not return something that is aliased to one of the inputs.
-        output[0] = np.asarray(0, dtype="int64")
-
-
-minimal = Minimal()

pytensor/tensor/rewriting/__init__.py

@@ -7,6 +7,7 @@ import pytensor.tensor.rewriting.extra_ops
 
 # Register JAX specializations
 import pytensor.tensor.rewriting.jax
+import pytensor.tensor.rewriting.linalg
 import pytensor.tensor.rewriting.math
 import pytensor.tensor.rewriting.shape
 import pytensor.tensor.rewriting.special

tests/sandbox/test_minimal.py

-import numpy as np
-import pytest
-
-from pytensor import function
-from pytensor.sandbox.minimal import minimal
-from pytensor.tensor.type import matrix, vector
-from tests import unittest_tools as utt
-
-
-@pytest.mark.skip(reason="Unfinished test")
-class TestMinimal:
-    """
-    TODO: test dtype conversion
-    TODO: test that invalid types are rejected by make_node
-    TODO: test that each valid type for A and b works correctly
-    """
-
-    def setup_method(self):
-        self.rng = np.random.default_rng(utt.fetch_seed(666))
-
-    def test_minimal(self):
-        A = matrix()
-        b = vector()
-
-        print("building function")
-        f = function([A, b], minimal(A, A, b, b, A))
-        print("built")
-
-        Aval = self.rng.standard_normal((5, 5))
-        bval = np.arange(5, dtype=float)
-        f(Aval, bval)
-        print("done")

tests/sandbox/linalg/test_linalg.py → tests/tensor/rewriting/test_linalg.py

@@ -5,10 +5,10 @@ import pytensor
 from pytensor import function
 from pytensor import tensor as at
 from pytensor.configdefaults import config
-from pytensor.sandbox.linalg.ops import inv_as_solve, spectral_radius_bound
 from pytensor.tensor.elemwise import DimShuffle
 from pytensor.tensor.math import _allclose
 from pytensor.tensor.nlinalg import MatrixInverse, matrix_inverse
+from pytensor.tensor.rewriting.linalg import inv_as_solve
 from pytensor.tensor.slinalg import Cholesky, Solve, solve
 from pytensor.tensor.type import dmatrix, matrix, vector
 from tests import unittest_tools as utt
@@ -65,53 +65,6 @@ def test_rop_lop():
     assert _allclose(v1, v2), f"LOP mismatch: {v1} {v2}"
 
 
-def test_spectral_radius_bound():
-    tol = 10 ** (-6)
-    rng = np.random.default_rng(utt.fetch_seed())
-    x = matrix()
-    radius_bound = spectral_radius_bound(x, 5)
-    f = pytensor.function([x], radius_bound)
-
-    shp = (3, 4)
-    m = rng.random(shp)
-    m = np.cov(m).astype(config.floatX)
-    radius_bound_pytensor = f(m)
-
-    # test the approximation
-    mm = m
-    for i in range(5):
-        mm = np.dot(mm, mm)
-    radius_bound_numpy = np.trace(mm) ** (2 ** (-5))
-    assert abs(radius_bound_numpy - radius_bound_pytensor) < tol
-
-    # test the bound
-    eigen_val = numpy.linalg.eig(m)
-    assert (eigen_val[0].max() - radius_bound_pytensor) < tol
-
-    # test type errors
-    xx = vector()
-    ok = False
-    try:
-        spectral_radius_bound(xx, 5)
-    except TypeError:
-        ok = True
-    assert ok
-    ok = False
-    try:
-        spectral_radius_bound(x, 5.0)
-    except TypeError:
-        ok = True
-    assert ok
-
-    # test value error
-    ok = False
-    try:
-        spectral_radius_bound(x, -5)
-    except ValueError:
-        ok = True
-    assert ok
-
-
 def test_transinv_to_invtrans():
     X = matrix("X")
     Y = matrix_inverse(X)