Source code for cvxpy.expressions.constants.parameter

Copyright 2013 Steven Diamond

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
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distributed under the License is distributed on an "AS IS" BASIS,
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from __future__ import annotations

import cvxpy.lin_ops.lin_utils as lu
from cvxpy import settings as s
from cvxpy.expressions.leaf import Leaf
from cvxpy.utilities import scopes

def is_param_affine(expr) -> bool:
    """Returns true if expression is parameters-affine (and variable-free)"""
    with scopes.dpp_scope():
        return not expr.variables() and expr.is_affine()

def is_param_free(expr) -> bool:
    """Returns true if expression is not parametrized."""
    return not expr.parameters()

[docs]class Parameter(Leaf): """Parameters in optimization problems. Parameters are constant expressions whose value may be specified after problem creation. The only way to modify a problem after its creation is through parameters. For example, you might choose to declare the hyper-parameters of a machine learning model to be Parameter objects; more generally, Parameters are useful for computing trade-off curves. """ PARAM_COUNT = 0 def __init__( self, shape: int | tuple[int, ...] = (), name: str | None = None, value=None, id=None, **kwargs ) -> None: if id is None: = lu.get_id() else: = id if name is None: self._name = f"{s.PARAM_PREFIX}{}" else: self._name = name # Initialize with value if provided. self._value = None = None self.gradient = None super(Parameter, self).__init__(shape, value, **kwargs) self._is_constant = True def get_data(self): """Returns info needed to reconstruct the expression besides the args. """ return [self.shape, self._name, self.value,, self.attributes] def name(self) -> str: return self._name def is_constant(self) -> bool: if scopes.dpp_scope_active(): return False return True # Getter and setter for parameter value. @property def value(self): """NumPy.ndarray or None: The numeric value of the parameter. """ return self._value @value.setter def value(self, val): self._value = self._validate_value(val) @property def grad(self): """Gives the (sub/super)gradient of the expression w.r.t. each variable. Matrix expressions are vectorized, so the gradient is a matrix. Returns: A map of variable to SciPy CSC sparse matrix or None. """ return {} def parameters(self) -> list[Parameter]: """Returns itself as a parameter. """ return [self] def canonicalize(self): """Returns the graph implementation of the object. Returns: A tuple of (affine expression, [constraints]). """ obj = lu.create_param(self.shape, return (obj, []) def __repr__(self) -> str: """String to recreate the object. """ attr_str = self._get_attr_str() if len(attr_str) > 0: return "Parameter(%s%s)" % (self.shape, attr_str) else: return "Parameter(%s)" % (self.shape,)