ENH: make stacked exceptions clearer

pyblp/configurations/formulation.py

@@ -190,7 +190,8 @@ def _build_matrix(
 
                 if not fallback:
                     origin = patsy.origin.Origin(self._formula, 0, len(self._formula))
-                    raise patsy.PatsyError(f"Failed to load data for '{name}'.", origin) from exception
+                    message = f"Failed to load data for '{name}' because of the above exception."
+                    raise patsy.PatsyError(message, origin) from exception
 
         # always have at least one column to represent the size of the data
         if not data_mapping:
@@ -244,7 +245,8 @@ def _build_ids(self, data: Mapping) -> Array:
             except Exception as exception:
                 assert self._absorb is not None
                 origin = patsy.origin.Origin(self._absorb, 0, len(self._absorb))
-                raise patsy.PatsyError(f"Failed to load data for '{name}'.", origin) from exception
+                message = f"Failed to load data for '{name}' because of the above exception."
+                raise patsy.PatsyError(message, origin) from exception
 
         # build columns of absorbed IDs
         ids_columns: List[Array] = []
@@ -482,7 +484,8 @@ def parse_term_expression(term: patsy.desc.Term) -> sp.Expr:
         try:
             expression *= parse_expression(factor.name())
         except Exception as exception:
-            raise patsy.PatsyError("Failed to parse a term.", factor.origin) from exception
+            message = "Failed to parse a term because of the above exception."
+            raise patsy.PatsyError(message, factor.origin) from exception
 
     return expression
 
@@ -542,7 +545,7 @@ def validate_categorical(candidate: sp.Expr, depth: int = 0, categorical: bool =
         str(expression)
         validate_categorical(expression)
     except (TypeError, ValueError) as exception:
-        raise ValueError(f"The expression '{string}' is malformed.") from exception
+        raise ValueError(f"The expression '{string}' is malformed because of the above exception.") from exception
 
     # replace patsy functions with the identity function, unless categorical variables are to be explicitly marked
     for name in patsy_function_names:

pyblp/markets/market.py

@@ -1244,7 +1244,8 @@ def compute_micro_contributions(
             try:
                 weights = np.asarray(dataset.compute_weights(self.t, self.products, self.agents), options.dtype)
             except Exception as exception:
-                raise RuntimeError(f"Failed to compute weights for micro dataset '{dataset}'.") from exception
+                message = f"Failed to compute weights for micro dataset '{dataset}' because of the above exception."
+                raise RuntimeError(message) from exception
             shapes = [
                 (self.I, self.J), (self.I, 1 + self.J), (self.I, self.J, self.J), (self.I, 1 + self.J, self.J),
                 (self.I, self.J, 1 + self.J), (self.I, 1 + self.J, 1 + self.J)
@@ -1440,7 +1441,8 @@ def compute_micro_contributions(
             try:
                 values = np.asarray(moment.compute_values(self.t, self.products, self.agents), options.dtype)
             except Exception as exception:
-                raise RuntimeError(f"Failed to compute values for micro moment '{moment}'.") from exception
+                message = f"Failed to compute values for micro moment '{moment}' because of the above exception."
+                raise RuntimeError(message) from exception
             if values.shape != weights.shape:
                 raise ValueError(
                     f"In market {self.t}, micro moment '{moment}' returned an array of shape {values.shape} from "
@@ -1467,7 +1469,10 @@ def compute_micro_contributions(
                                 moment2.compute_values(self.t, self.products, self.agents), options.dtype
                             )
                         except Exception as exception:
-                            raise RuntimeError(f"Failed to compute values for micro moment '{moment2}'.") from exception
+                            message = (
+                                f"Failed to compute values for micro moment '{moment2}' because of the above exception."
+                            )
+                            raise RuntimeError(message) from exception
                         micro_covariances_numerator[m, m2] = (weighted_values * values2).sum()
 
         return (

pyblp/micro.py

@@ -205,7 +205,8 @@ def __init__(self, economy: 'Economy', micro_moments: Sequence[MicroMoment]) ->
             try:
                 moment.dataset._validate(economy)
             except Exception as exception:
-                raise ValueError(f"The micro dataset '{moment.dataset}' is invalid.") from exception
+                message = f"The micro dataset '{moment.dataset}' is invalid because of the above exception."
+                raise ValueError(message) from exception
             for moment2 in micro_moments[:m]:
                 if moment == moment2:
                     raise ValueError(f"There is more than one of the micro moment '{moment}'.")

pyblp/primitives.py

@@ -297,11 +297,12 @@ def __new__(
                         except patsy.PatsyError as exception_j:
                             if j == 0:
                                 raise exception
-                            raise ValueError(
+                            message = (
                                 f"Each demographic must either be a single column or have a column for each of the "
                                 f"maximum of {max_J} products. There is at least one missing demographic for product "
                                 f"index {j}."
-                            ) from exception_j
+                            )
+                            raise ValueError(message) from exception_j
                         else:
                             demographics_by_product.append(demographics_j)