Merge pull request #272 from jakobj/fix/fec-cache-key

Also use all args and kwargs when computing fec cache key

cgp/utils.py

@@ -65,8 +65,8 @@ def __compute_key_from_args(*args: Any, **kwargs: Any) -> str:
     return hashlib.sha1(s.encode("utf-8")).hexdigest()
 
 
-def __compute_key_from_evaluation(
-    seed: int, min_value: float, max_value: float, batch_size: int, *args: Any
+def __compute_key_from_evaluation_and_args(
+    seed: int, min_value: float, max_value: float, batch_size: int, *args: Any, **kwargs: Any
 ) -> str:
     """Compute a key for the function encoded in an individual by
     evaluating it's NumPy expression on random input samples and
@@ -96,6 +96,8 @@ def __compute_key_from_evaluation(
     else:
         assert False  # should never be reached
 
+    s += str(args[1:]) + str(kwargs)
+
     return hashlib.sha1(s.encode("utf-8")).hexdigest()
 
 
@@ -222,8 +224,8 @@ def wrapper(*args: Any, **kwargs: Any) -> Union[float, None]:
 
             key: str
             if use_fec:
-                key = __compute_key_from_evaluation(
-                    fec_seed, fec_min_value, fec_max_value, fec_batch_size, *args
+                key = __compute_key_from_evaluation_and_args(
+                    fec_seed, fec_min_value, fec_max_value, fec_batch_size, *args, **kwargs
                 )
             else:
                 key = __compute_key_from_args(*args, **kwargs)

test/test_utils.py

@@ -380,3 +380,30 @@ def test_primitives_from_class_names_for_genome(genome_params):
     genome_params["primitives"] = primitives
 
     cgp.Genome(**genome_params)
+
+
+def test_fec_cache_decorator_with_additional_arguments(genome_params, rng, rng_seed):
+    def f_target(x):
+        return x[0] - x[1]
+
+    @cgp.utils.disk_cache(tempfile.mkstemp()[1], use_fec=True, fec_seed=rng_seed)
+    def inner_objective(ind, n_samples):
+        np.random.seed(rng_seed)
+
+        f = ind.to_func()
+
+        loss = 0
+        for x in np.random.uniform(size=(n_samples, 2)):
+            loss += (f_target(x) - f(x)[0]) ** 2
+        return loss
+
+    g = cgp.Genome(**genome_params)
+    g.randomize(rng)
+    ind = cgp.IndividualSingleGenome(g)
+
+    # call with 5 and 10 samples, results should be different since
+    # the second call should *not* return the cached value
+    y0 = inner_objective(ind, 5)
+    y1 = inner_objective(ind, 10)
+
+    assert y0 != pytest.approx(y1)