Coverage for cvx/risk/bounds.py: 100%

1# Copyright 2023 Stanford University Convex Optimization Group 

2# 

3# Licensed under the Apache License, Version 2.0 (the "License"); 

4# you may not use this file except in compliance with the License. 

5# You may obtain a copy of the License at 

6# 

7# http://www.apache.org/licenses/LICENSE-2.0 

8# 

9# Unless required by applicable law or agreed to in writing, software 

10# distributed under the License is distributed on an "AS IS" BASIS, 

11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 

12# See the License for the specific language governing permissions and 

13# limitations under the License. 

14"""Bounds""" 

15from __future__ import annotations 

16 

17from dataclasses import dataclass 

18 

19import cvxpy as cp 

20import numpy as np 

21 

22from .model import Model 

23 

24 

25@dataclass 

26class Bounds(Model): 

27 m: int = 0 

28 name: str = "" 

29 

30 def estimate(self, weights, **kwargs): 

31 """No estimation for bounds""" 

32 raise NotImplementedError("No estimation for bounds") 

33 

34 def _f(self, str): 

35 return f"{str}_{self.name}" 

36 

37 def __post_init__(self): 

38 self.parameter[self._f("lower")] = cp.Parameter( 

39 shape=self.m, 

40 name="lower bound", 

41 value=np.zeros(self.m), 

42 ) 

43 self.parameter[self._f("upper")] = cp.Parameter( 

44 shape=self.m, 

45 name="upper bound", 

46 value=np.ones(self.m), 

47 ) 

48 

49 def update(self, **kwargs): 

50 # lower = kwargs.get("lower", np.zeros(self.m)) 

51 lower = kwargs[self._f("lower")] 

52 self.parameter[self._f("lower")].value = np.zeros(self.m) 

53 self.parameter[self._f("lower")].value[: len(lower)] = lower 

54 

55 upper = kwargs[self._f("upper")] # .get("upper", np.ones(self.m)) 

56 self.parameter[self._f("upper")].value = np.zeros(self.m) 

57 self.parameter[self._f("upper")].value[ 

58 : len(upper) 

59 ] = upper # kwargs.get("upper", np.ones(m)) 

60 

61 def constraints(self, weights, **kwargs): 

62 return [ 

63 weights >= self.parameter[self._f("lower")], 

64 weights <= self.parameter[self._f("upper")], 

65 ]