refactor name

alphamind/analysis/factoranalysis.py

@@ -16,7 +16,7 @@ from alphamind.portfolio.constraints import Constraints
 from alphamind.portfolio.constraints import LinearConstraints
 from alphamind.portfolio.longshortbulder import long_short_build
 from alphamind.portfolio.rankbuilder import rank_build
-from alphamind.portfolio.linearbuilder import linear_build
+from alphamind.portfolio.linearbuilder import linear_builder
 from alphamind.portfolio.meanvariancebuilder import mean_variance_builder
 from alphamind.portfolio.meanvariancebuilder import target_vol_builder
 from alphamind.data.processing import factor_processing
@@ -95,7 +95,7 @@ def er_portfolio_analysis(er: np.ndarray,
         turn_over_target = kwargs.get('turn_over_target')
         current_position = kwargs.get('current_position')
 
-        status, _, weights = linear_build(er,
+        status, _, weights = linear_builder(er,
                                             risk_constraints=cons_exp,
                                             lbound=lbound,
                                             ubound=ubound,

alphamind/benchmarks/portfolio/linearbuild.py

@@ -10,7 +10,7 @@ import numpy as np
 from scipy.optimize import linprog
 from cvxopt import matrix
 from cvxopt import solvers
-from alphamind.portfolio.linearbuilder import linear_build
+from alphamind.portfolio.linearbuilder import linear_builder
 
 solvers.options['show_progress'] = False
 
@@ -33,7 +33,7 @@ def benchmark_build_linear(n_samples: int, n_risks: int, n_loop: int) -> None:
 
     start = dt.datetime.now()
     for _ in range(n_loop):
-        status, v, x = linear_build(er,
+        status, v, x = linear_builder(er,
                                       lbound,
                                       ubound,
                                       risk_exp,

alphamind/portfolio/linearbuilder.py

@@ -11,7 +11,7 @@ from typing import Union
 from alphamind.cython.optimizers import LPOptimizer
 
 
-def linear_build(er: np.ndarray,
+def linear_builder(er: np.ndarray,
                    lbound: Union[np.ndarray, float],
                    ubound: Union[np.ndarray, float],
                    risk_constraints: np.ndarray,
@@ -100,7 +100,7 @@ if __name__ == '__main__':
     risk_lbound = np.ones(1)
     risk_ubound = np.ones(1)
 
-    status, fvalue, x_values = linear_build(er,
+    status, fvalue, x_values = linear_builder(er,
                                               lb,
                                               ub,
                                               cons,

alphamind/tests/portfolio/test_linearbuild.py

@@ -7,7 +7,7 @@ Created on 2017-5-5
 
 import unittest
 import numpy as np
-from alphamind.portfolio.linearbuilder import linear_build
+from alphamind.portfolio.linearbuilder import linear_builder
 
 
 class TestLinearBuild(unittest.TestCase):
@@ -23,7 +23,7 @@ class TestLinearBuild(unittest.TestCase):
         bm = self.bm / self.bm.sum()
         eplson = 1e-6
 
-        status, _, w = linear_build(self.er,
+        status, _, w = linear_builder(self.er,
                                       0.,
                                       0.01,
                                       self.risk_exp,
@@ -49,7 +49,7 @@ class TestLinearBuild(unittest.TestCase):
         risk_lbound[:-1] = risk_lbound[:-1] - risk_tolerance
         risk_ubound[:-1] = risk_ubound[:-1] + risk_tolerance
 
-        status, _, w = linear_build(self.er,
+        status, _, w = linear_builder(self.er,
                                       0.,
                                       0.01,
                                       self.risk_exp,
@@ -75,7 +75,7 @@ class TestLinearBuild(unittest.TestCase):
         risk_lbound[:-1] = risk_lbound[:-1] - risk_tolerance
         risk_ubound[:-1] = risk_ubound[:-1] + risk_tolerance
 
-        status, _, w = linear_build(self.er,
+        status, _, w = linear_builder(self.er,
                                       0.,
                                       0.01,
                                       self.risk_exp,