Clip negative values to to avoid NaN correlation term

bpl/_util.py

@@ -10,6 +10,7 @@ def dixon_coles_correlation_term(
     home_rate: jnp.array,
     away_rate: jnp.array,
     corr_coef: jnp.array,
+    tol: float = 0,  # FIXME workaround to clip negative values to tol to avoid NaNs
 ) -> jnp.array:
     # correlation term from dixon and coles paper
     corr_term = jnp.zeros_like(home_rate)
@@ -19,28 +20,39 @@ def dixon_coles_correlation_term(
         corr_term,
         (..., nil_nil),
         jnp.log(
-            1.0
-            - corr_coef[..., None] * home_rate[..., nil_nil] * away_rate[..., nil_nil]
+            jnp.clip(
+                1.0
+                - corr_coef[..., None]
+                * home_rate[..., nil_nil]
+                * away_rate[..., nil_nil],
+                a_min=tol,
+            )
         ),
     )
 
     one_nil = (home_goals == 1) & (away_goals == 0)
     corr_term = jax.ops.index_update(
         corr_term,
         (..., one_nil),
-        jnp.log(1.0 + corr_coef[..., None] * away_rate[..., one_nil]),
+        jnp.log(
+            jnp.clip(1.0 + corr_coef[..., None] * away_rate[..., one_nil], a_min=tol)
+        ),
     )
 
     nil_one = (home_goals == 0) & (away_goals == 1)
     corr_term = jax.ops.index_update(
         corr_term,
         (..., nil_one),
-        jnp.log(1.0 + corr_coef[..., None] * home_rate[..., nil_one]),
+        jnp.log(
+            jnp.clip(1.0 + corr_coef[..., None] * home_rate[..., nil_one], a_min=tol)
+        ),
     )
 
     one_one = (home_goals == 1) & (away_goals == 1)
     corr_term = jax.ops.index_update(
-        corr_term, (..., one_one), jnp.log(1.0 - corr_coef[..., None])
+        corr_term,
+        (..., one_one),
+        jnp.log(jnp.clip(1.0 - corr_coef[..., None], a_min=tol)),
     )
 
     return corr_term

bpl/base.py

@@ -155,4 +155,3 @@ def predict_concede_n_proba(
 
         # sum probability all scorelines where team conceded n goals
         return probs.sum(axis=0)
-

bpl/dixon_coles.py

@@ -12,8 +12,8 @@
 from numpyro.infer import MCMC, NUTS
 from numpyro.infer.reparam import LocScaleReparam
 
-from bpl.base import BaseMatchPredictor
 from bpl._util import dixon_coles_correlation_term
+from bpl.base import BaseMatchPredictor
 
 __all__ = ["DixonColesMatchPredictor"]
 

bpl/extended_dixon_coles.py

@@ -12,8 +12,8 @@
 from numpyro.infer import MCMC, NUTS
 from numpyro.infer.reparam import LocScaleReparam
 
-from bpl.base import BaseMatchPredictor
 from bpl._util import dixon_coles_correlation_term
+from bpl.base import BaseMatchPredictor
 
 __all__ = ["ExtendedDixonColesMatchPredictor"]