Added new transforms submodules and fill_scale_tril.py transform to c… (

#5)

* Added new transforms submodules and fill_scale_tril.py transform to convert vectors into lower triangular matrices

* Tests now handle nested batches. FillScaleTriL uses .diagonal_scatter instead of .copy_

* Removed unused imports

* Wrote individual FillTriL, DiagTransform. Wrote composition FillScaleTriL

* Added test for `FillScaleTriL().log_abs_det_jacobian`

* Fixed documentation errors

* added `params_size` and default AffineTransform shift to `FillScaleTriL`

* Update fill_scale_tril.py

* Update fill_scale_tril.py

* Update fill_scale_tril.py

* Update fill_scale_tril.py

* Update fill_scale_tril.py

* Update fill_scale_tril.py

src/rs_distributions/transforms/__init__.py

@@ -0,0 +1,7 @@
+from .fill_scale_tril import FillScaleTriL, FillTriL, DiagTransform
+
+__all__ = [
+    "FillScaleTriL",
+    "FillTriL",
+    "DiagTransform",
+]

src/rs_distributions/transforms/fill_scale_tril.py

@@ -0,0 +1,128 @@
+import torch
+from torch.distributions import Transform, ComposeTransform, constraints
+from torch.distributions.transforms import SoftplusTransform, AffineTransform
+from torch.distributions.utils import vec_to_tril_matrix, tril_matrix_to_vec
+
+
+class FillTriL(Transform):
+    """
+    Transform for converting a real-valued vector into a lower triangular matrix
+    """
+
+    def __init__(self):
+        super().__init__()
+
+    @property
+    def domain(self):
+        return constraints.real_vector
+
+    @property
+    def codomain(self):
+        return constraints.lower_triangular
+
+    @property
+    def bijective(self):
+        return True
+
+    def _call(self, x):
+        """
+        Converts real-valued vector to lower triangular matrix.
+
+        Args:
+            x (torch.Tensor): input real-valued vector
+        Returns:
+            torch.Tensor: Lower triangular matrix
+        """
+
+        return vec_to_tril_matrix(x)
+
+    def _inverse(self, y):
+        return tril_matrix_to_vec(y)
+
+    def log_abs_det_jacobian(self, x, y):
+        batch_shape = x.shape[:-1]
+        return torch.zeros(batch_shape, dtype=x.dtype, device=x.device)
+
+
+class DiagTransform(Transform):
+    """
+    Applies transformation to the diagonal of a square matrix
+    """
+
+    def __init__(self, diag_transform):
+        super().__init__()
+        self.diag_transform = diag_transform
+
+    @property
+    def domain(self):
+        return self.diag_transform.domain
+
+    @property
+    def codomain(self):
+        return self.diag_transform.codomain
+
+    @property
+    def bijective(self):
+        return self.diag_transform.bijective
+
+    def _call(self, x):
+        """
+        Args:
+            x (torch.Tensor): Input matrix
+        Returns
+            torch.Tensor: Transformed matrix
+        """
+        diagonal = x.diagonal(dim1=-2, dim2=-1)
+        transformed_diagonal = self.diag_transform(diagonal)
+        result = x.diagonal_scatter(transformed_diagonal, dim1=-2, dim2=-1)
+
+        return result
+
+    def _inverse(self, y):
+        diagonal = y.diagonal(dim1=-2, dim2=-1)
+        result = y.diagonal_scatter(self.diag_transform.inv(diagonal), dim1=-2, dim2=-1)
+        return result
+
+    def log_abs_det_jacobian(self, x, y):
+        diagonal = x.diagonal(dim1=-2, dim2=-1)
+        return self.diag_transform.log_abs_det_jacobian(diagonal, y)
+
+
+class FillScaleTriL(ComposeTransform):
+    """
+    A `ComposeTransform` that reshapes a real-valued vector into a lower triangular matrix.
+    The diagonal of the matrix is transformed with `diag_transform`.
+    """
+
+    def __init__(self, diag_transform=None):
+        if diag_transform is None:
+            diag_transform = torch.distributions.ComposeTransform(
+                (
+                    SoftplusTransform(),
+                    AffineTransform(1e-5, 1.0),
+                )
+            )
+        super().__init__([FillTriL(), DiagTransform(diag_transform=diag_transform)])
+        self.diag_transform = diag_transform
+
+    @property
+    def bijective(self):
+        return True
+
+    def log_abs_det_jacobian(self, x, y):
+        x = FillTriL()._call(x)
+        diagonal = x.diagonal(dim1=-2, dim2=-1)
+        return self.diag_transform.log_abs_det_jacobian(diagonal, diagonal)
+
+    @staticmethod
+    def params_size(event_size):
+        """
+        Returns the number of parameters required to create an n-by-n lower triangular matrix, which is given by n*(n+1)//2
+
+        Args:
+            event_size (int): size of event
+        Returns:
+            int: Number of parameters needed
+
+        """
+        return event_size * (event_size + 1) // 2

tests/transforms/fill_scale_tril.py

@@ -0,0 +1,73 @@
+import pytest
+from rs_distributions.transforms.fill_scale_tril import (
+    FillScaleTriL,
+    FillTriL,
+    DiagTransform,
+)
+import torch
+from torch.distributions.constraints import lower_cholesky
+from torch.distributions.transforms import SoftplusTransform, ExpTransform
+
+
+@pytest.mark.parametrize("batch_shape, d", [((2, 3), 6), ((1, 4, 5), 10)])
+def test_forward_transform(batch_shape, d):
+    transform = FillScaleTriL()
+    input_shape = batch_shape + (d,)
+    input_vector = torch.randn(input_shape)
+    transformed_vector = transform(input_vector)
+
+    n = int((-1 + torch.sqrt(torch.tensor(1 + 8 * d))) / 2)
+    expected_output_shape = batch_shape + (n, n)
+    cholesky_constraint_check = lower_cholesky.check(transformed_vector)
+
+    assert isinstance(transformed_vector, torch.Tensor), "Output is not a torch.Tensor"
+    assert (
+        transformed_vector.shape == expected_output_shape
+    ), f"Expected shape {expected_output_shape}, got {transformed_vector.shape}"
+    assert cholesky_constraint_check.all()
+
+
+@pytest.mark.parametrize("batch_shape, d", [((2, 3), 6), ((1, 4, 5), 10)])
+def test_forward_equals_inverse(batch_shape, d):
+    transform = FillScaleTriL()
+    input_shape = batch_shape + (d,)
+    input_vector = torch.randn(input_shape)
+    L = transform(input_vector)
+    invL = transform.inv(L)
+
+    assert torch.allclose(
+        input_vector, invL, atol=1e-4
+    ), "Original input and the result of applying inverse transformation are not close enough"
+
+
+@pytest.mark.parametrize(
+    "batch_shape, d, diag_transform",
+    [
+        ((2, 3), 6, SoftplusTransform()),
+        ((1, 4, 5), 10, SoftplusTransform()),
+        ((2, 3), 6, ExpTransform()),
+        ((1, 4, 5), 10, ExpTransform()),
+    ],
+)
+def test_log_abs_det_jacobian_softplus_and_exp(batch_shape, d, diag_transform):
+    transform = FillScaleTriL(diag_transform=diag_transform)
+    filltril = FillTriL()
+    diagtransform = DiagTransform(diag_transform=diag_transform)
+    input_shape = batch_shape + (d,)
+    input_vector = torch.randn(input_shape, requires_grad=True)
+    transformed_vector = transform(input_vector)
+
+    # Calculate gradients log_abs_det_jacobian from FillScaleTriL
+    log_abs_det_jacobian = transform.log_abs_det_jacobian(
+        input_vector, transformed_vector
+    )
+
+    # Extract diagonal elements from input and transformed vectors
+    tril = filltril(input_vector)
+    diagonal_transformed = diagtransform(tril)
+
+    # Calculate diagonal gradients
+    diag_jacobian = diagtransform.log_abs_det_jacobian(tril, diagonal_transformed)
+
+    # Assert diagonal gradients are approximately equal
+    assert torch.allclose(diag_jacobian, log_abs_det_jacobian, atol=1e-4)