mdo_function module¶
Base class to describe a function.
- class gemseo.core.mdofunctions.mdo_function.MDOFunction(func, name, f_type=FunctionType.NONE, jac=None, expr='', input_names=None, dim=0, output_names=None, force_real=False, special_repr='', original_name='')[source]
Bases:
Serializable
The standard definition of an array-based function with algebraic operations.
MDOFunction
is the key class to define the objective function, the constraints and the observables of anOptimizationProblem
.an
MDOFunction
is initialized from an optional callable and a name, e.g.func = MDOFunction(lambda x: 2*x, "my_function")
.Note
The callable can be set to
None
when the user does not want to use a callable but a database to browse for the output vector corresponding to an input vector (seeMDOFunction.set_pt_from_database()
).The following information can also be provided at initialization:
the type of the function, e.g.
f_type="obj"
if the function will be used as an objective (seeMDOFunction.FunctionType
),the function computing the Jacobian matrix, e.g.
jac=lambda x: array([2.])
,the literal expression to be used for the string representation of the object, e.g.
expr="2*x"
,the names of the inputs and outputs of the function, e.g.
input_names=["x"]
andoutput_names=["y"]
.
Warning
For the literal expression, do not use “f(x) = 2*x” nor “f = 2*x” but “2*x”. The other elements will be added automatically in the string representation of the function based on the name of the function and the names of its inputs.
After the initialization, all of these arguments can be overloaded with setters, e.g.
MDOFunction.input_names
.The original function and Jacobian function can be accessed with the properties
MDOFunction.func
andMDOFunction.jac
.an
MDOFunction
is callable:output = func(array([3.])) # expected: array([6.])
.Elementary operations can be performed with
MDOFunction
instances: addition (func = func1 + func2
orfunc = func1 + offset
), subtraction (func = func1 - func2
orfunc = func1 - offset
), multiplication (func = func1 * func2
orfunc = func1 * factor
) and opposite (func = -func1
). It is also possible to build anMDOFunction
as a concatenation ofMDOFunction
objects:func = MDOFunction.concatenate([func1, func2, func3], "my_func_123"
).Moreover, an
MDOFunction
can be approximated with either a first-order or second-order Taylor polynomial at a given input vector, using respectivelyMDOFunction.linear_approximation()
andquadratic_approx()
; such an approximation is also anMDOFunction
.Lastly, the user can check the Jacobian function by means of approximation methods (see
MDOFunction.check_grad()
).Initialize self. See help(type(self)) for accurate signature.
- Parameters:
func (WrappedFunctionType | None) – The original function to be actually called. If
None
, the function will not have an original function.name (str) – The name of the function.
f_type (FunctionType) –
The type of the function.
By default it is set to “”.
jac (WrappedJacobianType | None) – The original Jacobian function to be actually called. If
None
, the function will not have an original Jacobian function.expr (str) –
The expression of the function, e.g. “2*x”, if any.
By default it is set to “”.
input_names (Iterable[str] | None) – The names of the inputs of the function. If
None
, the inputs of the function will have no names.dim (int) –
The dimension of the output space of the function. If 0, the dimension of the output space of the function will be deduced from the evaluation of the function.
By default it is set to 0.
output_names (Iterable[str] | None) – The names of the outputs of the function. If
None
, the outputs of the function will have no names.force_real (bool) –
Whether to cast the output values to real.
By default it is set to False.
special_repr (str) –
The string representation of the function. If empty, use
default_repr()
.By default it is set to “”.
original_name (str) –
The original name of the function. If empty, use the same name than the
name
input.By default it is set to “”.
- class ApproximationMode(value)
Bases:
StrEnum
The approximation derivation modes.
- CENTERED_DIFFERENCES = 'centered_differences'
The centered differences method used to approximate the Jacobians by perturbing each variable with a small real number.
- COMPLEX_STEP = 'complex_step'
The complex step method used to approximate the Jacobians by perturbing each variable with a small complex number.
- FINITE_DIFFERENCES = 'finite_differences'
The finite differences method used to approximate the Jacobians by perturbing each variable with a small real number.
- class ConstraintType(value)[source]
Bases:
StrEnum
The type of constraint.
- EQ = 'eq'
The type of function for equality constraint.
- INEQ = 'ineq'
The type of function for inequality constraint.
- class FunctionType(value)
Bases:
StrEnum
An enumeration.
- EQ = 'eq'
- INEQ = 'ineq'
- NONE = ''
- OBJ = 'obj'
- OBS = 'obs'
- check_grad(x_vect, approximation_mode=ApproximationMode.FINITE_DIFFERENCES, step=1e-06, error_max=1e-08)[source]
Check the gradients of the function.
- Parameters:
x_vect (ndarray[Any, dtype[Number]]) – The vector at which the function is checked.
approximation_mode (ApproximationMode) –
The approximation mode.
By default it is set to “finite_differences”.
step (float) –
The step for the approximation of the gradients.
By default it is set to 1e-06.
error_max (float) –
The maximum value of the error.
By default it is set to 1e-08.
- Raises:
ValueError – Either if the approximation method is unknown, if the shapes of the analytical and approximated Jacobian matrices are inconsistent or if the analytical gradients are wrong.
- Return type:
None
- evaluate(x_vect)[source]
Evaluate the function and store the dimension of the output space.
- static filt_0(arr, floor_value=1e-06)[source]
Set the non-significant components of a vector to zero.
The component of a vector is non-significant if its absolute value is lower than a threshold.
- static from_pickle(file_path)[source]
Deserialize a function from a file.
- Parameters:
file_path (str | Path) – The path to the file containing the function.
- Returns:
The function instance.
- Return type:
- classmethod generate_input_names(input_dim, input_names=None)[source]
Generate the names of the inputs of the function.
- Parameters:
input_dim (int) – The dimension of the input space of the function.
input_names (Sequence[str] | None) – The initial names of the inputs of the function. If there is only one name, e.g.
["var"]
, use this name as a base name and generate the names of the inputs, e.g.["var!0", "var!1", "var!2"]
if the dimension of the input space is equal to 3. IfNone
, use"x"
as a base name and generate the names of the inputs, i.e.["x!0", "x!1", "x!2"]
.
- Returns:
The names of the inputs of the function.
- Return type:
Sequence[str]
- get_indexed_name(index)[source]
Return the name of function component.
- static init_from_dict_repr(**attributes)[source]
Initialize a new function.
This is typically used for deserialization.
- Parameters:
**attributes (Any) – The values of the serializable attributes listed in
MDOFunction.DICT_REPR_ATTR
.- Returns:
A function initialized from the provided data.
- Raises:
ValueError – If the name of an argument is not in
MDOFunction.DICT_REPR_ATTR
.- Return type:
- is_constraint()[source]
Check if the function is a constraint.
The type of a constraint function is either ‘eq’ or ‘ineq’.
- Returns:
Whether the function is a constraint.
- Return type:
- offset(value)[source]
Add an offset value to the function.
- static rel_err(a_vect, b_vect, error_max)[source]
Compute the 2-norm of the difference between two vectors.
Normalize it with the 2-norm of the reference vector if the latter is greater than the maximal error.
- set_pt_from_database(database, design_space, normalize=False, jac=True, x_tolerance=1e-10)[source]
Set the original function and Jacobian function from a database.
For a given input vector, the method
MDOFunction.func()
will return either the output vector stored in the database if the input vector is present orNone
. The same for the methodMDOFunction.jac()
.- Parameters:
database (Database) – The database to read.
design_space (DesignSpace) – The design space used for normalization.
normalize (bool) –
If
True
, the values of the inputs are unnormalized before call.By default it is set to False.
jac (bool) –
If
True
, a Jacobian pointer is also generated.By default it is set to True.
x_tolerance (float) –
The tolerance on the distance between inputs.
By default it is set to 1e-10.
- Return type:
None
- to_dict()[source]
Create a dictionary representation of the function.
This is used for serialization. The pointers to the functions are removed.
- to_pickle(file_path)[source]
Serialize the function and store it in a file.
- Parameters:
file_path (str | Path) – The path to the file to store the function.
- Return type:
None
- COEFF_FORMAT_1D: str = '{:.2e}'
The format to be applied to a number when represented in a vector.
- COEFF_FORMAT_ND: str = '{: .2e}'
The format to be applied to a number when represented in a matrix.
- DEFAULT_BASE_INPUT_NAME: str = 'x'
The default base name for the inputs.
- DICT_REPR_ATTR: ClassVar[list[str]] = ['name', 'f_type', 'expr', 'input_names', 'dim', 'special_repr', 'output_names']
The names of the attributes to be serialized.
- INDEX_PREFIX: str = '!'
The character used to separate a name base and a prefix, e.g.
"x!1
.
- activate_counters: ClassVar[bool] = True
Whether to count the number of function evaluations.
- property default_repr: str
The default string representation of the function.
- property dim: int
The dimension of the output space of the function.
- property expects_normalized_inputs: bool
Whether the functions expect normalized inputs or not.
- property expr: str
The expression of the function, e.g. “2*x”.
- force_real: bool
Whether to cast the results to real value.
- property func: Callable[[ndarray[Any, dtype[Number]]], ndarray[Any, dtype[Number]] | Number]
The function to be evaluated from a given input vector.
- has_default_name: bool
Whether the name has been set with a default value.
- property has_jac: bool
Check if the function has an implemented Jacobian function.
- Returns:
Whether the function has an implemented Jacobian function.
- property input_names: list[str]
The names of the inputs of the function.
Use a copy of the original names.
- property jac: Callable[[ndarray[Any, dtype[Number]]], ndarray[Any, dtype[Number]]]
The Jacobian function to be evaluated from a given input vector.
- last_eval: OutputType | None
The value of the function output at the last evaluation.
None
if it has not yet been evaluated.
- property n_calls: int
The number of times the function has been evaluated.
This count is both multiprocess- and multithread-safe, thanks to the locking process used by
MDOFunction.evaluate()
.
- property name: str
The name of the function.
- property original_name: str
The original name of the function.
- property output_names: list[str]
The names of the outputs of the function.
Use a copy of the original names.
- special_repr: str
The string representation of the function overloading its default string ones.
Examples using MDOFunction¶
Post-process an optimization problem
Save an optimization problem for post-processing