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weatherbench2/evaluation.py

@@ -390,6 +390,7 @@ def _metric_and_region_loop(
 ) -> xr.Dataset:
   """Compute metric results looping over metrics and regions in eval config."""
   # Compute derived variables
+  logging.info('Starting _metric_and_region_loop')
   for name, dv in eval_config.derived_variables.items():
     logging.info(f'Logging: derived_variable {name!r}: {dv}')
     forecast[name] = dv.compute(forecast)

weatherbench2/metrics.py

@@ -24,6 +24,7 @@
 import numpy as np
 from scipy import stats
 from weatherbench2 import thresholds
+from weatherbench2 import utils
 from weatherbench2.regions import Region
 import xarray as xr
 
@@ -705,6 +706,7 @@ def compute_chunk(
     return _pointwise_crps_skill(forecast, truth, self.ensemble_dim)
 
 
+@utils.id_lru_cache(maxsize=1)
 def _pointwise_crps_spread(
     forecast: xr.Dataset, truth: xr.Dataset, ensemble_dim: str
 ) -> xr.Dataset:

weatherbench2/utils.py

@@ -13,7 +13,8 @@
 # limitations under the License.
 # ==============================================================================
 """Utility function for WeatherBench2."""
-from typing import Callable, Union
+import functools
+from typing import Callable, Hashable, Union
 
 import fsspec
 import numpy as np
@@ -292,3 +293,48 @@ def random_like(dataset: xr.Dataset, seed: int = 0) -> xr.Dataset:
   return dataset.copy(
       data={k: rs.normal(size=v.shape) for k, v in dataset.items()}
   )
+
+
+def id_lru_cache(maxsize: int = 5):
+  """Like functools.lru_cache but uses argument id for non-hashables.
+
+  Warning: This is not threadsafe. Multiple threads reading/writing to the cache
+  results in inconsistent behavior.
+
+  Args:
+    maxsize: Maximum size of cache.
+
+  Returns:
+    Decorator to make a function into a caching function.
+  """
+
+  def hashid(x):
+    if isinstance(x, Hashable):
+      return hash(x)
+    return id(x)
+
+  def decorating_function(func):
+    cache = {}
+
+    @functools.wraps(func)
+    def wrapper(*args, **kwargs):
+      key = tuple(hashid(a) for a in args) + tuple(
+          (k, hashid(v)) for k, v in kwargs.items()
+      )
+      # Python dicts are ordered in the sense that if keys = list(my_dict.key())
+      # then keys[0] is the first key added, and keys[-1] is the most recently
+      # added.
+      if key in cache:
+        # Move cache[key] to position -1 since it is most recently used.
+        value = cache[key]
+        del cache[key]
+        cache[key] = value
+      else:
+        if len(cache) >= maxsize:
+          cache.pop(list(cache)[0])  # Pop first item added to dictionary.
+        cache[key] = func(*args, **kwargs)
+      return cache[key]
+
+    return wrapper
+
+  return decorating_function

weatherbench2/utils_test.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 from absl.testing import absltest
+import numpy as np
 from weatherbench2 import schema
 from weatherbench2 import utils
 import xarray
@@ -67,5 +68,39 @@ def testProbabilisticClimatology(self):
     self.assertEqual(clim['2m_temperature'].sizes, expected_sizes)
 
 
+class IdLRUCacheTest(absltest.TestCase):
+
+  def test_handles_non_hashable_args_and_kwargs(self):
+
+    @utils.id_lru_cache(maxsize=2)
+    def func(x: np.ndarray, y: np.ndarray, b: float = 1):
+      return np.sum(x + y * b)
+
+    # Use 3 sets of arrays so we are sure to cycle through the size 2 cache.
+    with self.subTest('First set of arrays'):
+      x = np.array([1.0, 2.0, 3.0])
+      y = x + 2
+      b = 1.3
+      expected = np.sum(x + y * b)
+      for _ in range(4):
+        self.assertEqual(expected, func(x, y, b=b))
+
+    with self.subTest('Second set of arrays'):
+      x = np.array([0.0, -2.0, 0.123])
+      y = np.array([10.0, -1.0, 3])
+      b = 10.3
+      expected = np.sum(x + y * b)
+      for _ in range(4):
+        self.assertEqual(expected, func(x, y, b=b))
+
+    with self.subTest('Third set of arrays'):
+      x = np.array([0.0, -20.0])
+      y = np.array([10.0, -11.0])
+      b = -1234
+      expected = np.sum(x + y * b)
+      for _ in range(4):
+        self.assertEqual(expected, func(x, y, b=b))
+
+
 if __name__ == '__main__':
   absltest.main()