Refactor Cython variables

zillow · Sep 7, 2024 · a030857 · a030857
1 parent 86ea424
commit a030857
Showing 1 changed file with 7 additions and 7 deletions.
diff --git a/quantile_forest/_quantile_forest_fast.pyx b/quantile_forest/_quantile_forest_fast.pyx
@@ -606,6 +606,7 @@ cdef class QuantileForest:
         cdef intp_t n_trees, n_train, max_idx
         cdef intp_t i, j, k, l
         cdef bint use_mean
+        cdef double q
         cdef list[char*] interpolations
         cdef vector[double] leaf_samples
         cdef vector[double] leaf_weights
@@ -615,7 +616,6 @@ cdef class QuantileForest:
         cdef double train_wgt
         cdef vector[int] n_leaf_samples
         cdef int n_total_samples, n_total_trees
-        cdef double train_weight
         cdef vector[vector[double]] leaf_preds
         cdef vector[double] pred
         cdef cnp.ndarray[float64_t, ndim=3] preds
@@ -702,15 +702,15 @@ cdef class QuantileForest:
                         for k in range(n_trees):
                             if X_indices is None or X_indices[i, k] is True:
                                 idx = 0 if aggregate_leaves_first else k
-                                train_weight = 1
+                                train_wgt = 1
                                 if weighted_leaves:
-                                    train_weight = 0
+                                    train_wgt = 0
                                     if n_leaf_samples[k] > 0:
-                                        train_weight = 1 / <double>n_leaf_samples[k]
-                                        train_weight *= <double>n_total_samples
-                                        train_weight /= <double>n_total_trees
+                                        train_wgt = 1 / <double>n_leaf_samples[k]
+                                        train_wgt *= <double>n_total_samples
+                                        train_wgt /= <double>n_total_trees
                                 train_weights[idx].insert(
-                                    train_weights[idx].end(), max_idx, train_weight
+                                    train_weights[idx].end(), max_idx, train_wgt
                                 )
 
                         # For each list of training indices, calculate output.