Fix python warm-start cloglog residual corruption bug

Author: andrewherren

CHANGELOG.md

@@ -9,7 +9,7 @@
 
 ## Bug Fixes
 
-* Fixed initialization and probability prediction bugs for cloglog in R [#357](https://github.com/StochasticTree/stochtree/pull/357)
+* Fixed several initialization, sampling and prediction bugs for cloglog [#357](https://github.com/StochasticTree/stochtree/pull/357)
 * Fixed num_threads pass-through bug in R BART [#339](https://github.com/StochasticTree/stochtree/pull/339)
 * Fixed probit offset bug in R and Python [#337](https://github.com/StochasticTree/stochtree/pull/337)
 * Standardized multi-chain BCF handling of internal propensity models [#334](https://github.com/StochasticTree/stochtree/pull/334)

NEWS.md

@@ -7,7 +7,7 @@
 
 ## Bug Fixes
 
-* Fixed initialization and probability prediction bugs for cloglog in R [#357](https://github.com/StochasticTree/stochtree/pull/357)
+* Fixed several initialization, sampling and prediction bugs for cloglog [#357](https://github.com/StochasticTree/stochtree/pull/357)
 * Fixed num_threads pass-through bug in R BART [#339](https://github.com/StochasticTree/stochtree/pull/339)
 * Fixed probit offset bug in R and Python [#337](https://github.com/StochasticTree/stochtree/pull/337)
 * Standardized multi-chain BCF handling of internal propensity models [#334](https://github.com/StochasticTree/stochtree/pull/334)

stochtree/bart.py

@@ -1699,6 +1699,9 @@ def sample(
                             residual_train,
                             True,
                         )
+                        # Undo residual update done by reconstitute_from_forest for the cloglog model
+                        if link_is_cloglog:
+                          residual_train.update_data(resid_train[:, 0])
                         # Reset leaf scale
                         if sample_sigma2_leaf:
                                 leaf_scale_double = self.leaf_scale_samples[
@@ -1754,6 +1757,9 @@ def sample(
                             residual_train,
                             True,
                         )
+                        # Undo residual update done by reconstitute_from_forest for the cloglog model
+                        if link_is_cloglog:
+                          residual_train.update_data(resid_train[:, 0])
                         # Reset leaf scale
                         if sample_sigma2_leaf and previous_leaf_var_samples is not None:
                             leaf_scale_double = previous_leaf_var_samples[
@@ -1818,6 +1824,9 @@ def sample(
                             residual_train,
                             True,
                         )
+                        # Undo residual update done by reconstitute_from_forest for the cloglog model
+                        if link_is_cloglog:
+                          residual_train.update_data(resid_train[:, 0])
                         # Reset mean forest leaf scale
                         if sample_sigma2_leaf and previous_leaf_var_samples is not None:
                             current_leaf_scale[0, 0] = sigma2_leaf