Merge pull request #239 from StochasticTree/python-arg-order-hotfix

Update argument order in Python BART and BCF

Author: andrewherren

stochtree/bart.py

@@ -2193,25 +2193,19 @@ def compute_contrast(
 
     def compute_posterior_interval(
         self,
-        terms: Union[list[str], str] = "all",
-        level: float = 0.95,
-        scale: str = "linear",
         X: np.array = None,
         leaf_basis: np.array = None,
         rfx_group_ids: np.array = None,
         rfx_basis: np.array = None,
+        terms: Union[list[str], str] = "all",
+        level: float = 0.95,
+        scale: str = "linear",
     ) -> dict:
         """
         Compute posterior credible intervals for specified terms from a fitted BART model. It supports intervals for mean functions, variance functions, random effects, and overall predictions.
 
         Parameters
         ----------
-        terms : str, optional
-            Character string specifying the model term(s) for which to compute intervals. Options for BART models are `"mean_forest"`, `"variance_forest"`, `"rfx"`, `"y_hat"`, or `"all"`. Defaults to `"all"`.
-        scale : str, optional
-            Scale of mean function predictions. Options are "linear", which returns predictions on the original scale of the mean forest / RFX terms, and "probability", which transforms predictions into a probability of observing `y == 1`. "probability" is only valid for models fit with a probit outcome model. Defaults to `"linear"`.
-        level : float, optional
-            A numeric value between 0 and 1 specifying the credible interval level. Defaults to 0.95 for a 95% credible interval.
         X : np.array, optional
             Optional array or data frame of covariates at which to compute the intervals. Required if the requested term depends on covariates (e.g., mean forest, variance forest, or overall predictions).
         leaf_basis : np.array, optional
@@ -2220,6 +2214,12 @@ def compute_posterior_interval(
             Optional vector of group IDs for random effects. Required if the requested term includes random effects.
         rfx_basis : np.array, optional
             Optional matrix of basis function evaluations for random effects. Required if the requested term includes random effects.
+        terms : str, optional
+            Character string specifying the model term(s) for which to compute intervals. Options for BART models are `"mean_forest"`, `"variance_forest"`, `"rfx"`, `"y_hat"`, or `"all"`. Defaults to `"all"`.
+        scale : str, optional
+            Scale of mean function predictions. Options are "linear", which returns predictions on the original scale of the mean forest / RFX terms, and "probability", which transforms predictions into a probability of observing `y == 1`. "probability" is only valid for models fit with a probit outcome model. Defaults to `"linear"`.
+        level : float, optional
+            A numeric value between 0 and 1 specifying the credible interval level. Defaults to 0.95 for a 95% credible interval.
 
         Returns
         -------

stochtree/bcf.py

@@ -3260,26 +3260,20 @@ def compute_contrast(
 
     def compute_posterior_interval(
         self,
-        terms: Union[list[str], str] = "all",
-        level: float = 0.95,
-        scale: str = "linear",
         X: np.array = None,
         Z: np.array = None,
         propensity: np.array = None,
         rfx_group_ids: np.array = None,
         rfx_basis: np.array = None,
+        terms: Union[list[str], str] = "all",
+        level: float = 0.95,
+        scale: str = "linear",
     ) -> dict:
         """
         Compute posterior credible intervals for specified terms from a fitted BART model. It supports intervals for mean functions, variance functions, random effects, and overall predictions.
 
         Parameters
         ----------
-        terms : str, optional
-            Character string specifying the model term(s) for which to compute intervals. Options for BCF models are `"prognostic_function"`, `"mu"`, `"cate"`, `"tau"`, `"variance_forest"`, `"rfx"`, or `"y_hat"`. Defaults to `"all"`. Note that `"mu"` is only different from `"prognostic_function"` if random effects are included with a model spec of `"intercept_only"` or `"intercept_plus_treatment"` and `"tau"` is only different from `"cate"` if random effects are included with a model spec of `"intercept_plus_treatment"`.
-        scale : str, optional
-            Scale of mean function predictions. Options are "linear", which returns predictions on the original scale of the mean forest / RFX terms, and "probability", which transforms predictions into a probability of observing `y == 1`. "probability" is only valid for models fit with a probit outcome model. Defaults to `"linear"`.
-        level : float, optional
-            A numeric value between 0 and 1 specifying the credible interval level. Defaults to 0.95 for a 95% credible interval.
         X : np.array, optional
             Optional array or data frame of covariates at which to compute the intervals. Required if the requested term depends on covariates (e.g., prognostic forest, treatment effect forest, variance forest, or overall predictions).
         Z : np.array, optional
@@ -3290,6 +3284,12 @@ def compute_posterior_interval(
             Optional vector of group IDs for random effects. Required if the requested term includes random effects.
         rfx_basis : np.array, optional
             Optional matrix of basis function evaluations for random effects. Required if the requested term includes random effects.
+        terms : str, optional
+            Character string specifying the model term(s) for which to compute intervals. Options for BCF models are `"prognostic_function"`, `"mu"`, `"cate"`, `"tau"`, `"variance_forest"`, `"rfx"`, or `"y_hat"`. Defaults to `"all"`. Note that `"mu"` is only different from `"prognostic_function"` if random effects are included with a model spec of `"intercept_only"` or `"intercept_plus_treatment"` and `"tau"` is only different from `"cate"` if random effects are included with a model spec of `"intercept_plus_treatment"`.
+        scale : str, optional
+            Scale of mean function predictions. Options are "linear", which returns predictions on the original scale of the mean forest / RFX terms, and "probability", which transforms predictions into a probability of observing `y == 1`. "probability" is only valid for models fit with a probit outcome model. Defaults to `"linear"`.
+        level : float, optional
+            A numeric value between 0 and 1 specifying the credible interval level. Defaults to 0.95 for a 95% credible interval.
 
         Returns
         -------