Implement Cholesky for covariance handling

.github/workflows/ci-workflow.yml

@@ -13,7 +13,7 @@ concurrency:
 
 jobs:
   test:
-    name: py${{ matrix.python-version }} ${{ matrix.env }}
+    name: py${{ matrix.python-version }} ${{ matrix.os }} ${{ matrix.env }}
     runs-on: ${{ matrix.os }}
     strategy:
       fail-fast: false
@@ -25,6 +25,12 @@ jobs:
           - env: "min-all-deps"
             python-version: "3.9"
             os: "ubuntu-latest"
+          - env: ""
+            python-version: "3.12"
+            os: "macos-latest"
+          - env: ""
+            python-version: "3.12"
+            os: "windows-latest"
     defaults:
       run:
         shell: bash -l {0}

CHANGELOG.rst

@@ -11,7 +11,8 @@ New Features
 
 Breaking changes
 ^^^^^^^^^^^^^^^^
-
+- Using Cholesky decomposition for finding covariance localization radius and drawing from the multivariate normal distribution (`#408 <https://github.com/MESMER-group/mesmer/pull/408>`_)
+  By `Victoria Bauer`_.
 - The supported versions of some dependencies were changed (`#399 <https://github.com/MESMER-group/mesmer/pull/399>`_, `#405 <https://github.com/MESMER-group/mesmer/pull/405>`_):
 
   ============ ============= =========

mesmer/stats/_auto_regression.py

@@ -1,8 +1,11 @@
+import warnings
+
 import numpy as np
 import pandas as pd
+import scipy
 import xarray as xr
 
-from mesmer.core.utils import _check_dataarray_form, _check_dataset_form
+from mesmer.core.utils import LinAlgWarning, _check_dataarray_form, _check_dataset_form
 
 
 def _select_ar_order_scen_ens(*objs, dim, ens_dim, maxlag, ic="bic"):
@@ -479,11 +482,24 @@ def _draw_auto_regression_correlated_np(
 
     # NOTE: 'innovations' is the error or noise term.
     # innovations has shape (n_samples, n_ts + buffer, n_coeffs)
-    innovations = np.random.multivariate_normal(
+    try:
+        cov = scipy.stats.Covariance.from_cholesky(np.linalg.cholesky(covariance))
+    except np.linalg.LinAlgError as e:
+        if "Matrix is not positive definite" in str(e):
+            w, v = np.linalg.eigh(covariance)
+            cov = scipy.stats.Covariance.from_eigendecomposition((w, v))
+            warnings.warn(
+                "Covariance matrix is not positive definite, using eigh instead of cholesky.",
+                LinAlgWarning,
+            )
+        else:
+            raise
+
+    innovations = scipy.stats.multivariate_normal.rvs(
         mean=np.zeros(n_coeffs),
-        cov=covariance,
+        cov=cov,
         size=[n_samples, n_ts + buffer],
-    )
+    ).reshape(n_samples, n_ts + buffer, n_coeffs)
 
     out = np.zeros([n_samples, n_ts + buffer, n_coeffs])
     for t in range(ar_order + 1, n_ts + buffer):

mesmer/stats/_localized_covariance.py

@@ -1,6 +1,7 @@
 import warnings
 
 import numpy as np
+import scipy
 import xarray as xr
 
 from mesmer.core.utils import (
@@ -267,12 +268,11 @@ def _get_neg_loglikelihood(data, covariance, weights):
     The mean is assumed to be zero for all points.
     """
 
-    from scipy.stats import multivariate_normal
-
     # NOTE: 90 % of time is spent in multivariate_normal.logpdf - not much point
     # optimizing the rest
 
-    log_likelihood = multivariate_normal.logpdf(data, cov=covariance)
+    cov = scipy.stats.Covariance.from_cholesky(np.linalg.cholesky(covariance))
+    log_likelihood = scipy.stats.multivariate_normal.logpdf(data, cov=cov)
 
     # logpdf can return a scalar, which np.average does not like
     log_likelihood = np.atleast_1d(log_likelihood)

tests/integration/test_draw_realisations_from_bundle.py

@@ -102,29 +102,10 @@ def test_make_realisations(
     else:
         exp = xr.open_dataset(expected_output_file)
 
-        # check that less than 10% of output differs. Something weird is
-        # happening with numpy's random seed (we get different values
-        # depending on the operating system) so we currently can't do any
-        # better than this.
-        _assert_frac_allclose(result, exp, rtol=1e-4, wrong_tol=0.1)
-
-        # # Ideally we would use the below, but we can't because of numpy's
-        # # random seed issue (see comment above).
-        # xr.testing.assert_allclose(result, exp, rtol=rtol)
+        xr.testing.assert_allclose(result, exp)
 
         # make sure we can get onto a lat lon grid from what is saved
         exp_reshaped = exp.set_index(z=("lat", "lon")).unstack("z")
         expected_dims = {"scenario", "realisation", "lon", "lat", "year"}
 
         assert set(exp_reshaped.dims) == expected_dims
-
-
-def _assert_frac_allclose(result, expected, rtol, wrong_tol):
-    # check that less than wrong_tol of output differs
-
-    for v in expected.data_vars:
-
-        differing_spots = ~np.isclose(result[v].values, expected[v].values, rtol=rtol)
-
-        frac_differing = differing_spots.sum() / result[v].values.size
-        assert frac_differing < wrong_tol, v

tests/unit/test_auto_regression.py

@@ -6,7 +6,7 @@
 import xarray as xr
 
 import mesmer
-from mesmer.core.utils import _check_dataarray_form, _check_dataset_form
+from mesmer.core.utils import LinAlgWarning, _check_dataarray_form, _check_dataset_form
 from mesmer.testing import trend_data_1D, trend_data_2D, trend_data_3D
 
 
@@ -79,7 +79,7 @@ def ar_params_1D():
 
     intercept = xr.DataArray(0)
     coeffs = xr.DataArray([0], dims="lags")
-    variance = xr.DataArray(0)
+    variance = xr.DataArray(0.5)
     ar_params = xr.Dataset(
         {"intercept": intercept, "coeffs": coeffs, "variance": variance}
     )
@@ -92,7 +92,7 @@ def ar_params_2D():
 
     intercept = xr.DataArray([0, 0], dims="gridcell")
     coeffs = xr.DataArray([[0, 0]], dims=("lags", "gridcell"))
-    variance = xr.DataArray([0, 0], dims="gridcell")
+    variance = xr.DataArray([0.5, 0.3], dims="gridcell")
     ar_params = xr.Dataset(
         {"intercept": intercept, "coeffs": coeffs, "variance": variance}
     )
@@ -345,7 +345,7 @@ def test_draw_auto_regression_correlated_np_shape(ar_order, n_cells, n_samples,
 
     intercept = np.zeros(n_cells)
     coefs = np.ones((ar_order, n_cells))
-    variance = np.ones((n_cells, n_cells))
+    variance = np.eye(n_cells)
 
     result = mesmer.stats._auto_regression._draw_auto_regression_correlated_np(
         intercept=intercept,
@@ -362,6 +362,9 @@ def test_draw_auto_regression_correlated_np_shape(ar_order, n_cells, n_samples,
     assert result.shape == expected_shape
 
 
+@pytest.mark.filterwarnings(
+    "ignore:Covariance matrix is not positive definite, using eigh instead of cholesky."
+)
 @pytest.mark.parametrize("intercept", [0, 1, 3.14])
 def test_draw_auto_regression_deterministic_intercept(intercept):
 
@@ -394,6 +397,9 @@ def test_draw_auto_regression_deterministic_intercept(intercept):
     np.testing.assert_equal(result, expected)
 
 
+@pytest.mark.filterwarnings(
+    "ignore:Covariance matrix is not positive definite, using eigh instead of cholesky."
+)
 def test_draw_auto_regression_deterministic_coefs_buffer():
 
     result = mesmer.stats._auto_regression._draw_auto_regression_correlated_np(
@@ -444,6 +450,25 @@ def test_draw_auto_regression_random():
     np.testing.assert_allclose(result, expected)
 
 
+def test_draw_auto_regression_correlated_eigh():
+    # test that the function uses eigh when the covariance matrix is not positive definite
+    with pytest.warns(
+        LinAlgWarning, match="Covariance matrix is not positive definite"
+    ):
+        result = mesmer.stats._auto_regression._draw_auto_regression_correlated_np(
+            intercept=1,
+            coeffs=np.array([[0.5, 0.7], [0.3, 0.2]]),
+            covariance=np.zeros((2, 2)),
+            n_samples=1,
+            n_ts=4,
+            seed=0,
+            buffer=3,
+        )
+
+    expected = np.array([[[1.0, 1.0], [1.5, 1.7], [2.05, 2.39], [2.475, 3.013]]])
+    np.testing.assert_allclose(result, expected)
+
+
 @pytest.mark.parametrize("obj", [xr.Dataset(), None])
 def test_fit_auto_regression_xr_errors(obj):