simplify codes

alphamind/data/neutralize.py

@@ -46,15 +46,6 @@ def neutralize(x: np.ndarray, y: np.ndarray, groups: np.ndarray=None, output_exp
                 for i in range(explained.shape[2]):
                     explained[curr_idx] = ls_explain(curr_x, b)
             start = diff_loc + 1
-
-        curr_idx = order[start:]
-        curr_x, b = _sub_step(x, y, curr_idx, res)
-        if output_exposure:
-            for i in range(exposure.shape[2]):
-                exposure[curr_idx, :, i] = b[:, i]
-        if output_explained:
-            for i in range(explained.shape[2]):
-                explained[curr_idx] = ls_explain(curr_x, b)
     else:
         b = ls_fit(x, y)
         res = ls_res(x, y, b)

alphamind/model/linearmodel.py

@@ -60,8 +60,6 @@ def _train_loop(index_diff, order, x, y):
     for k, diff_loc in enumerate(index_diff):
         res_beta[k] = _train_sub_group(x, y, order[start:diff_loc + 1])
         start = diff_loc + 1
-
-    res_beta[k + 1] = _train_sub_group(x, y, order[start:])
     return res_beta
 
 

alphamind/portfolio/percentbuilder.py

@@ -29,12 +29,6 @@ def percent_build(er: np.ndarray, percent: float, groups: np.ndarray=None) -> np
                 use_rank = int(percent * len(current_index))
                 set_value(weights, current_index[current_ordering[:use_rank]], 1.)
                 start = diff_loc + 1
-
-            current_index = order[start:]
-            current_ordering = neg_er[current_index].argsort()
-            current_ordering.shape = -1, 1
-            use_rank = int(percent * len(current_index))
-            set_value(weights, current_index[current_ordering[:use_rank]], 1.)
         else:
             ordering = neg_er.argsort()
             use_rank = int(percent * len(neg_er))
@@ -53,10 +47,6 @@ def percent_build(er: np.ndarray, percent: float, groups: np.ndarray=None) -> np
                 use_rank = int(percent * len(current_index))
                 set_value(weights, current_index[current_ordering[:use_rank]], 1)
                 start = diff_loc + 1
-            current_index = order[start:]
-            current_ordering = neg_er[current_index].argsort(axis=0)
-            use_rank = int(percent * len(current_index))
-            set_value(weights, current_index[current_ordering[:use_rank]], 1)
         else:
             ordering = neg_er.argsort(axis=0)
             use_rank = int(percent * len(neg_er))

alphamind/portfolio/rankbuilder.py

@@ -28,11 +28,6 @@ def rank_build(er: np.ndarray, use_rank: int, groups: np.ndarray=None) -> np.nda
                 current_ordering.shape = -1, 1
                 set_value(weights, current_index[current_ordering[:use_rank]], 1.)
                 start = diff_loc + 1
-
-            current_index = order[start:]
-            current_ordering = neg_er[current_index].argsort()
-            current_ordering.shape = -1, 1
-            set_value(weights, current_index[current_ordering[:use_rank]], 1.)
         else:
             ordering = neg_er.argsort()
             weights[ordering[:use_rank]] = 1.
@@ -49,10 +44,6 @@ def rank_build(er: np.ndarray, use_rank: int, groups: np.ndarray=None) -> np.nda
                 current_ordering = neg_er[current_index].argsort(axis=0)
                 set_value(weights, current_index[current_ordering[:use_rank]], 1)
                 start = diff_loc + 1
-
-            current_index = order[start:]
-            current_ordering = neg_er[current_index].argsort(axis=0)
-            set_value(weights, current_index[current_ordering[:use_rank]], 1)
         else:
             ordering = neg_er.argsort(axis=0)
             set_value(weights, ordering[:use_rank], 1.)

alphamind/utilities.py

@@ -18,7 +18,7 @@ def groupby(groups):
     order = groups.argsort()
     t = groups[order]
     index_diff = np.where(np.diff(t))[0]
-    return index_diff, order
+    return np.concatenate([index_diff, [len(groups)]]), order
 
 
 @nb.njit(nogil=True, cache=True)