[ADD] Vanilla Hutchinson trace estimation

.readthedocs.yaml

@@ -6,10 +6,14 @@ version: 2
 sphinx:
  configuration: docs/rtd/conf.py
 
+build:
+ os: ubuntu-22.04
+ tools:
+ python: "3.8"
+
 python:
- version: "3.8"
  install:
-  - method: pip
-  path: .
-  extra_requirements:
-  - docs
+ - method: pip
+ path: .
+ extra_requirements:
+ - docs

curvlinops/__init__.py

@@ -13,6 +13,7 @@
  lanczos_approximate_spectrum,
 )
 from curvlinops.submatrix import SubmatrixLinearOperator
+from curvlinops.trace.hutchinson import HutchinsonTraceEstimator
 
 __all__ = [
  "HessianLinearOperator",
@@ -28,4 +29,5 @@
  "lanczos_approximate_log_spectrum",
  "LanczosApproximateSpectrumCached",
  "LanczosApproximateLogSpectrumCached",
+ "HutchinsonTraceEstimator",
 ]

curvlinops/trace/__init__.py

@@ -0,0 +1 @@
+"""Trace estimation techniques."""

curvlinops/trace/hutchinson.py

@@ -0,0 +1,85 @@
+"""Vanilla Hutchinson trace estimation."""
+
+from typing import Callable, Dict
+
+from numpy import dot, ndarray
+from scipy.sparse.linalg import LinearOperator
+
+from curvlinops.trace.sampling import normal, rademacher
+
+
+class HutchinsonTraceEstimator:
+ """Class to perform trace estimation with Hutchinson's method.
+
+ For details, see
+
+ - Hutchinson, M. (1989). A stochastic estimator of the trace of the influence
+ matrix for laplacian smoothing splines. Communication in Statistics---Simulation
+ and Computation.
+
+ Example:
+ >>> from numpy import trace, mean, round
+ >>> from numpy.random import rand, seed
+ >>> seed(0) # make deterministic
+ >>> A = rand(10, 10)
+ >>> tr_A = trace(A) # exact trace as reference
+ >>> estimator = HutchinsonTraceEstimator(A)
+ >>> # one- and multi-sample approximations
+ >>> tr_A_low_precision = estimator.sample()
+ >>> tr_A_high_precision = mean([estimator.sample() for _ in range(1_000)])
+ >>> assert abs(tr_A - tr_A_low_precision) > abs(tr_A - tr_A_high_precision)
+ >>> round(tr_A, 4), round(tr_A_low_precision, 4), round(tr_A_high_precision, 4)
+ (4.4575, 6.6796, 4.3886)
+
+ Attributes:
+ SUPPORTED_DISTRIBUTIONS: Dictionary mapping supported distributions to their
+ sampling functions.
+ """
+
+ SUPPORTED_DISTRIBUTIONS: Dict[str, Callable[[int], ndarray]] = {
+ "rademacher": rademacher,
+ "normal": normal,
+ }
+
+ def __init__(self, A: LinearOperator):
+ """Store the linear operator whose trace will be estimated.
+
+ Args:
+ A: Linear square-shaped operator whose trace will be estimated.
+
+ Raises:
+ ValueError: If the operator is not square.
+ """
+ if len(A.shape) != 2 or A.shape[0] != A.shape[1]:
+ raise ValueError(f"A must be square. Got shape {A.shape}.")
+ self._A = A
+
+ def sample(self, distribution: str = "rademacher") -> float:
+ """Draw a sample from the trace estimator.
+
+ Multiple samples can be combined into a more accurate trace estimation via
+ averaging.
+
+ Args:
+ distribution: Distribution of the vector along which the linear operator
+ will be evaluated. Either ``'rademacher'`` or ``'normal'``.
+ Default is ``'rademacher'``.
+
+ Returns:
+ Sample from the trace estimator.
+
+ Raises:
+ ValueError: If the distribution is not supported.
+ """
+ dim = self._A.shape[1]
+
+ if distribution not in self.SUPPORTED_DISTRIBUTIONS:
+ raise ValueError(
+ f"Unsupported distribution {distribution:!r}. "
+ f"Supported distributions are {list(self.SUPPORTED_DISTRIBUTIONS)}."
+ )
+
+ v = self.SUPPORTED_DISTRIBUTIONS[distribution](dim)
+ Av = self._A @ v
+
+ return dot(v, Av)

curvlinops/trace/sampling.py

@@ -0,0 +1,29 @@
+"""Sampling methods for random vectors."""
+
+from numpy import ndarray
+from numpy.random import binomial, randn
+
+
+def rademacher(dim: int) -> ndarray:
+ """Draw a vector with i.i.d. Rademacher elements.
+
+ Args:
+ dim: Dimension of the vector.
+
+ Returns:
+ Vector with i.i.d. Rademacher elements and specified dimension.
+ """
+ num_trials, success_prob = 1, 0.5
+ return binomial(num_trials, success_prob, size=dim).astype(float) * 2 - 1
+
+
+def normal(dim: int) -> ndarray:
+ """Drawa vector with i.i.d. standard normal elements.
+
+ Args:
+ dim: Dimension of the vector.
+
+ Returns:
+ Vector with i.i.d. standard normal elements and specified dimension.
+ """
+ return randn(dim)

docs/rtd/linops.rst

@@ -59,3 +59,9 @@ Spectral density approximation
 
 .. autoclass:: curvlinops.LanczosApproximateSpectrumCached
  :members: __init__, approximate_spectrum
+
+Trace approximation
+===================
+
+.. autoclass:: curvlinops.HutchinsonTraceEstimator
+ :members: __init__, sample

makefile

@@ -69,10 +69,10 @@ install-test:
 .PHONY: test test-light
 
 test:
- @pytest -vx --run-optional-tests=montecarlo --cov=curvlinops test
+ @pytest -vx --run-optional-tests=montecarlo --cov=curvlinops --doctest-modules curvlinops test
 
 test-light:
- @pytest -vx --cov=curvlinops test
+ @pytest -vx --cov=curvlinops --doctest-modules curvlinops test
 
 .PHONY: install-lint
 

test/trace/__init__.py

@@ -0,0 +1 @@
+"""Test ``curvlinops.trace``."""

test/trace/test_hutchinson.py

@@ -0,0 +1,43 @@
+"""Test ``curvlinops.trace.__init__``."""
+
+from numpy import isclose, mean, trace
+from numpy.random import rand, seed
+from pytest import mark
+
+from curvlinops import HutchinsonTraceEstimator
+
+DISTRIBUTIONS = ["rademacher", "normal"]
+DISTRIBUTION_IDS = [f"distribution={distribution}" for distribution in DISTRIBUTIONS]
+
+
+@mark.parametrize("distribution", DISTRIBUTIONS, ids=DISTRIBUTION_IDS)
+def test_HutchinsonTraceEstimator(distribution: str):
+ """Test whether Hutchinon's trace estimator converges to the true trace.
+
+ Args:
+ distribution: Distribution of the random vectors used for the trace estimation.
+ """
+ seed(0)
+ A = rand(10, 10)
+ tr_A = trace(A)
+
+ samples = []
+ max_samples = 20_000
+ chunk_size = 2_000 # add that many new samples before comparing against the truth
+ atol, rtol = 1e-3, 1e-2
+
+ estimator = HutchinsonTraceEstimator(A)
+
+ while len(samples) < max_samples:
+ samples.extend(
+ [estimator.sample(distribution=distribution) for _ in range(chunk_size)]
+ )
+ tr_estimator = mean(samples)
+ if not isclose(tr_A, tr_estimator, atol=atol, rtol=rtol):
+ print(f"{len(samples)} samples: Tr(A)={tr_A:.5f}≠{tr_estimator:.5f}.")
+ else:
+ # quit once the estimator has converged
+ break
+
+ tr_estimator = mean(samples)
+ assert isclose(tr_A, tr_estimator, atol=atol, rtol=rtol)