The GEMSEO concepts¶
Design space.
A design space is used to represent the optimization’s unknowns, a.k.a. design variables.
A DesignSpace
describes this design space at a given state,
in terms of names, sizes, types, bounds and current values of the design variables.
Variables can easily be added to the DesignSpace
using the DesignSpace.add_variable()
method
or removed using the DesignSpace.remove_variable()
method.
We can also filter the design variables using the DesignSpace.filter()
method.
Getters and setters are also available to get or set the value of a given variable property.
Lastly,
an instance of DesignSpace
can be stored in a txt or HDF file.
- class gemseo.algos.design_space.DesignSpace(hdf_file=None, name=None)[source]
Description of a design space.
It defines a set of variables from their names, sizes, types and bounds.
In addition, it provides the current values of these variables that can be used as the initial solution of an
OptimizationProblem
.A
DesignSpace
has the same API as a dictionary, e.g.variable = design_space["x"]
,other_design_space["x"] = design_space["x"]
,del design_space["x"]
,for name, value in design_space["x"].items()
, …- Parameters
hdf_file (str | Path | None) –
The path to the file containing the description of an initial design space. If None, start with an empty design space.
By default it is set to None.
name (str | None) –
The name to be given to the design space, None if the design space is unnamed.
By default it is set to None.
- Return type
None
- add_variable(name, size=1, var_type=DesignVariableType.FLOAT, l_b=None, u_b=None, value=None)[source]
Add a variable to the design space.
- Parameters
name (str) – The name of the variable.
size (int) –
The size of the variable.
By default it is set to 1.
var_type (VarType) –
Either the type of the variable or the types of its components.
By default it is set to FLOAT.
l_b (float | ndarray | None) –
The lower bound of the variable. If None, use \(-\infty\).
By default it is set to None.
u_b (float | ndarray | None) –
The upper bound of the variable. If None, use \(+\infty\).
By default it is set to None.
value (float | ndarray | None) –
The default value of the variable. If None, do not use a default value.
By default it is set to None.
- Raises
ValueError – Either if the variable already exists or if the size is not a positive integer.
- Return type
None
- array_to_dict(x_array)[source]
Convert a design array into a dictionary indexed by the variables names.
- Parameters
x_array (numpy.ndarray) – A design value expressed as a NumPy array.
- Returns
The design value expressed as a dictionary of NumPy arrays.
- Return type
- check()[source]
Check the state of the design space.
- Raises
ValueError – If the design space is empty.
- Return type
None
- check_membership(x_vect, variable_names=None)[source]
Check whether the variables satisfy the design space requirements.
- Parameters
- Raises
ValueError – Either if the dimension of the values vector is wrong, if the values are not specified as an array or a dictionary, if the values are outside the bounds of the variables or if the component of an integer variable is not an integer.
- Return type
None
- clear() None. Remove all items from D.
- dict_to_array(design_values, variable_names=None)[source]
Convert a point as dictionary into an array.
- Parameters
design_values (dict[str, numpy.ndarray]) – The design point to be converted.
variable_names (Optional[Iterable[str]]) –
The variables to be considered. If None, use the variables of the design space.
By default it is set to None.
- Returns
The point as an array.
- Return type
- export_hdf(file_path, append=False)[source]
Export the design space to an HDF file.
- export_to_txt(output_file, fields=None, header_char='', **table_options)[source]
Export the design space to a text file.
- Parameters
output_file (str | Path) – The path to the file.
fields (Sequence[str] | None) –
The fields to be exported. If None, export all fields.
By default it is set to None.
header_char (str) –
The header character.
By default it is set to .
**table_options (Any) – The names and values of additional attributes for the
PrettyTable
view generated byDesignSpace.get_pretty_table()
.
- Return type
None
- extend(other)[source]
Extend the design space with another design space.
- Parameters
other (gemseo.algos.design_space.DesignSpace) – The design space to be appended to the current one.
- Return type
None
- filter(keep_variables, copy=False)[source]
Filter the design space to keep a subset of variables.
- Parameters
- Returns
Either the filtered original design space or a copy.
- Raises
ValueError – If the variable is not in the design space.
- Return type
- filter_dim(variable, keep_dimensions)[source]
Filter the design space to keep a subset of dimensions for a variable.
- Parameters
- Returns
The filtered design space.
- Raises
ValueError – If a dimension is unknown.
- Return type
- get(k[, d]) D[k] if k in D, else d. d defaults to None.
- get_active_bounds(x_vec=None, tol=1e-08)[source]
Determine which bound constraints of a design value are active.
- Parameters
x_vec (ndarray | None) –
The design value at which to check the bounds. If
None
, use the current design value.By default it is set to None.
tol (float) –
The tolerance of comparison of a scalar with a bound.
By default it is set to 1e-08.
- Returns
Whether the components of the lower and upper bound constraints are active, the first returned value representing the lower bounds and the second one the upper bounds, e.g.
({'x': array(are_x_lower_bounds_active), 'y': array(are_y_lower_bounds_active)}, {'x': array(are_x_upper_bounds_active), 'y': array(are_y_upper_bounds_active)} )
where:
are_x_lower_bounds_active = [True, False] are_x_upper_bounds_active = [False, False] are_y_lower_bounds_active = [False] are_y_upper_bounds_active = [True]
- Return type
- get_current_value(variable_names=None, complex_to_real=False, as_dict=False, normalize=False)[source]
Return the current design value.
- Parameters
variable_names (Sequence[str] | None) –
The names of the design variables. If
None
, use all the design variables.By default it is set to None.
complex_to_real (bool) –
Whether to cast complex numbers to real ones.
By default it is set to False.
as_dict (bool) –
Whether to return the current design value as a dictionary of the form
{variable_name: variable_value}
.By default it is set to False.
normalize (bool) –
Whether to normalize the design values in \([0,1]\) with the bounds of the variables.
By default it is set to False.
- Returns
The current design value.
- Raises
KeyError – If a variable has no current value.
- Return type
- get_indexed_var_name(variable_name)[source]
Create the names of the components of a variable.
If the size of the variable is equal to 1, this method returns the name of the variable. Otherwise, it concatenates the name of the variable, the separator
DesignSpace.SEP
and the index of the component.
- get_indexed_variables_names()[source]
Create the names of the components of all the variables.
If the size of the variable is equal to 1, this method uses its name. Otherwise, it concatenates the name of the variable, the separator
DesignSpace.SEP
and the index of the component.
- get_lower_bound(name)[source]
Return the lower bound of a variable.
- Parameters
name (str) – The name of the variable.
- Returns
The lower bound of the variable (possibly infinite).
- Return type
- get_lower_bounds(variable_names=None)[source]
Generate an array of the variables’ lower bounds.
- Parameters
variable_names (Sequence[str] | None) –
The names of the variables of which the lower bounds are required. If None, use the variables of the design space.
By default it is set to None.
- Returns
The lower bounds of the variables.
- Return type
ndarray
- get_pretty_table(fields=None)[source]
Build a tabular view of the design space.
- Parameters
fields (Sequence[str] | None) –
The name of the fields to be exported. If None, export all the fields.
By default it is set to None.
- Returns
A tabular view of the design space.
- Return type
- get_size(name)[source]
Get the size of a variable.
- get_type(name)[source]
Return the type of a variable.
- get_upper_bound(name)[source]
Return the upper bound of a variable.
- Parameters
name (str) – The name of the variable.
- Returns
The upper bound of the variable (possibly infinite).
- Return type
- get_upper_bounds(variable_names=None)[source]
Generate an array of the variables’ upper bounds.
- Parameters
variable_names (Sequence[str] | None) –
The names of the variables of which the upper bounds are required. If None, use the variables of the design space.
By default it is set to None.
- Returns
The upper bounds of the variables.
- Return type
ndarray
- get_variables_indexes(variable_names)[source]
Return the indexes of a design array corresponding to the variables names.
- Parameters
variable_names (Iterable[str]) – The names of the variables.
- Returns
The indexes of a design array corresponding to the variables names.
- Return type
- has_current_value()[source]
Check if each variable has a current value.
- Returns
Whether the current design value is defined for all variables.
- Return type
- has_integer_variables()[source]
Check if the design space has at least one integer variable.
- Returns
Whether the design space has at least one integer variable.
- Return type
- import_hdf(file_path)[source]
Import a design space from an HDF file.
- Parameters
file_path (str | Path) – The path to the file containing the description of a design space.
- Return type
None
- items() a set-like object providing a view on D's items
- keys() a set-like object providing a view on D's keys
- normalize_grad(g_vect)[source]
Normalize an unnormalized gradient.
This method is based on the chain rule:
\[\frac{df(x)}{dx} = \frac{df(x)}{dx_u}\frac{dx_u}{dx} = \frac{df(x)}{dx_u}\frac{1}{u_b-l_b}\]where \(x_u = \frac{x-l_b}{u_b-l_b}\) is the normalized input vector, \(x\) is the unnormalized input vector and \(l_b\) and \(u_b\) are the lower and upper bounds of \(x\).
Then, the normalized gradient reads:
\[\frac{df(x)}{dx_u} = (u_b-l_b)\frac{df(x)}{dx}\]where \(\frac{df(x)}{dx}\) is the unnormalized one.
- Parameters
g_vect (numpy.ndarray) – The gradient to be normalized.
- Returns
The normalized gradient.
- Return type
- normalize_vect(x_vect, minus_lb=True, out=None)[source]
Normalize a vector of the design space.
If minus_lb is True:
\[x_u = \frac{x-l_b}{u_b-l_b}\]where \(l_b\) and \(u_b\) are the lower and upper bounds of \(x\).
Otherwise:
\[x_u = \frac{x}{u_b-l_b}\]Unbounded variables are not normalized.
- Parameters
x_vect (ndarray) – The values of the design variables.
minus_lb (bool) –
If True, remove the lower bounds at normalization.
By default it is set to True.
out (ndarray | None) –
The array to store the normalized vector. If None, create a new array.
By default it is set to None.
- Returns
The normalized vector.
- Return type
ndarray
- pop(k[, d]) v, remove specified key and return the corresponding value.
If key is not found, d is returned if given, otherwise KeyError is raised.
- popitem() (k, v), remove and return some (key, value) pair
as a 2-tuple; but raise KeyError if D is empty.
- project_into_bounds(x_c, normalized=False)[source]
Project a vector onto the bounds, using a simple coordinate wise approach.
- Parameters
normalized (bool) –
If True, then the vector is assumed to be normalized.
By default it is set to False.
x_c (numpy.ndarray) – The vector to be projected onto the bounds.
- Returns
The projected vector.
- Return type
- static read_from_txt(input_file, header=None)[source]
Create a design space from a text file.
- Parameters
- Returns
The design space read from the file.
- Raises
ValueError – If the file does not contain the minimal variables in its header.
- Return type
- remove_variable(name)[source]
Remove a variable from the design space.
- Parameters
name (str) – The name of the variable to be removed.
- Return type
None
- rename_variable(current_name, new_name)[source]
Rename a variable.
- round_vect(x_vect, copy=True)[source]
Round the vector where variables are of integer type.
- Parameters
x_vect (numpy.ndarray) – The values to be rounded.
copy (bool) –
Whether to round a copy of
x_vect
.By default it is set to True.
- Returns
The rounded values.
- Return type
- set_current_value(value)[source]
Set the current design value.
- Parameters
value (ndarray | Mapping[str, ndarray] | OptimizationResult) – The value of the current design.
- Raises
ValueError – If the value has a wrong dimension.
TypeError – If the value is neither a mapping of NumPy arrays, a NumPy array nor an
OptimizationResult
.
- Return type
None
- set_current_variable(name, current_value)[source]
Set the current value of a single variable.
- Parameters
name (str) – The name of the variable.
current_value (numpy.ndarray) – The current value of the variable.
- Return type
None
- set_lower_bound(name, lower_bound)[source]
Set the lower bound of a variable.
- Parameters
name (str) – The name of the variable.
lower_bound (numpy.ndarray) – The value of the lower bound.
- Raises
ValueError – If the variable does not exist.
- Return type
None
- set_upper_bound(name, upper_bound)[source]
Set the upper bound of a variable.
- Parameters
name (str) – The name of the variable.
upper_bound (numpy.ndarray) – The value of the upper bound.
- Raises
ValueError – If the variable does not exist.
- Return type
None
- setdefault(k[, d]) D.get(k,d), also set D[k]=d if k not in D
- to_complex()[source]
Cast the current value to complex.
- Return type
None
- transform_vect(vector, out=None)[source]
Map a point of the design space to a vector with components in \([0,1]\).
- Parameters
vector (ndarray) – A point of the design space.
out (ndarray | None) –
The array to store the transformed vector. If None, create a new array.
By default it is set to None.
- Returns
A vector with components in \([0,1]\).
- Return type
ndarray
- unnormalize_grad(g_vect)[source]
Unnormalize a normalized gradient.
This method is based on the chain rule:
\[\frac{df(x)}{dx} = \frac{df(x)}{dx_u}\frac{dx_u}{dx} = \frac{df(x)}{dx_u}\frac{1}{u_b-l_b}\]where \(x_u = \frac{x-l_b}{u_b-l_b}\) is the normalized input vector, \(x\) is the unnormalized input vector, \(\frac{df(x)}{dx_u}\) is the unnormalized gradient \(\frac{df(x)}{dx}\) is the normalized one, and \(l_b\) and \(u_b\) are the lower and upper bounds of \(x\).
- Parameters
g_vect (numpy.ndarray) – The gradient to be unnormalized.
- Returns
The unnormalized gradient.
- Return type
- unnormalize_vect(x_vect, minus_lb=True, no_check=False, out=None)[source]
Unnormalize a normalized vector of the design space.
If minus_lb is True:
\[x = x_u(u_b-l_b) + l_b\]where \(x_u\) is the normalized input vector, \(x\) is the unnormalized input vector and \(l_b\) and \(u_b\) are the lower and upper bounds of \(x\).
Otherwise:
\[x = x_u(u_b-l_b)\]- Parameters
x_vect (ndarray) – The values of the design variables.
minus_lb (bool) –
Whether to remove the lower bounds at normalization.
By default it is set to True.
no_check (bool) –
Whether to check if the components are in \([0,1]\).
By default it is set to False.
out (ndarray | None) –
The array to store the unnormalized vector. If None, create a new array.
By default it is set to None.
- Returns
The unnormalized vector.
- Return type
ndarray
- untransform_vect(vector, no_check=False, out=None)[source]
Map a vector with components in \([0,1]\) to the design space.
- Parameters
vector (ndarray) – A vector with components in \([0,1]\).
no_check (bool) –
Whether to check if the components are in \([0,1]\).
By default it is set to False.
out (ndarray | None) –
The array to store the untransformed vector. If None, create a new array.
By default it is set to None.
- Returns
A point of the variables space.
- Return type
ndarray
- update([E, ]**F) None. Update D from mapping/iterable E and F.
If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v
- values() an object providing a view on D's values
- dimension: int
The total dimension of the space, corresponding to the sum of the sizes of the variables.
- class gemseo.algos.design_space.DesignVariable(size, var_type, l_b, u_b, value)
Create new instance of DesignVariable(size, var_type, l_b, u_b, value)
- count(value, /)
Return number of occurrences of value.
- index(value, start=0, stop=9223372036854775807, /)
Return first index of value.
Raises ValueError if the value is not present.
- l_b
Alias for field number 2
- size
Alias for field number 0
- u_b
Alias for field number 3
- value
Alias for field number 4
- var_type
Alias for field number 1
- class gemseo.algos.design_space.DesignVariableType(value)[source]
A type of design variable.
Optimization problem.
The OptimizationProblem
class operates on a DesignSpace
defining:
an initial guess \(x_0\) for the design variables,
the bounds \(l_b \leq x \leq u_b\) of the design variables.
A (possible vector) objective function with a MDOFunction
type
is set using the objective
attribute.
If the optimization problem looks for the maximum of this objective function,
the OptimizationProblem.change_objective_sign()
changes the objective function sign
because the optimization drivers seek to minimize this objective function.
Equality and inequality constraints are also MDOFunction
instances
provided to the OptimizationProblem
by means of its OptimizationProblem.add_constraint()
method.
The OptimizationProblem
allows to evaluate the different functions
for a given design parameters vector
(see OptimizationProblem.evaluate_functions()
).
Note that this evaluation step relies on an automated scaling of function wrt the bounds
so that optimizers and DOE algorithms work
with inputs scaled between 0 and 1 for all the variables.
The OptimizationProblem
has also a Database
that stores the calls to all the functions
so that no function is called twice with the same inputs.
Concerning the derivatives’ computation,
the OptimizationProblem
automates
the generation of the finite differences or complex step wrappers on functions,
when the analytical gradient is not available.
Lastly,
various getters and setters are available,
as well as methods to export the Database
to an HDF file or to a Dataset
for future post-processing.
- class gemseo.algos.opt_problem.OptimizationProblem(design_space, pb_type='non-linear', input_database=None, differentiation_method='user', fd_step=1e-07, parallel_differentiation=False, use_standardized_objective=True, **parallel_differentiation_options)[source]
An optimization problem.
Create an optimization problem from:
a
DesignSpace
specifying the design variables in terms of names, lower bounds, upper bounds and initial guesses,the objective function as a
MDOFunction
, which can be a vector,
execute it from an algorithm provided by a
DriverLib
, and store some execution data in aDatabase
.In particular, this
Database
stores the calls to all the functions so that no function is called twice with the same inputs.An
OptimizationProblem
also has an automated scaling of function with respect to the bounds of the design variables so that the driving algorithms work with inputs scaled between 0 and 1.Lastly,
OptimizationProblem
automates the generation of finite differences or complex step wrappers on functions, when analytical gradient is not available.- Parameters
design_space (DesignSpace) – The design space on which the functions are evaluated.
pb_type (str) –
The type of the optimization problem among
OptimizationProblem.AVAILABLE_PB_TYPES
.By default it is set to non-linear.
input_database (str | Database | None) –
A database to initialize that of the optimization problem. If None, the optimization problem starts from an empty database.
By default it is set to None.
differentiation_method (str) –
The default differentiation method to be applied to the functions of the optimization problem.
By default it is set to user.
fd_step (float) –
The step to be used by the step-based differentiation methods.
By default it is set to 1e-07.
parallel_differentiation (bool) –
Whether to approximate the derivatives in parallel.
By default it is set to False.
use_standardized_objective (bool) –
Whether to use standardized objective for logging and post-processing.
By default it is set to True.
**parallel_differentiation_options (int | bool) – The options to approximate the derivatives in parallel.
- Return type
None
- add_callback(callback_func, each_new_iter=True, each_store=False)[source]
Add a callback function after each store operation or new iteration.
- Parameters
callback_func (Callable) – A function to be called after some event.
each_new_iter (bool) –
If True, then callback at every iteration.
By default it is set to True.
each_store (bool) –
If True, then callback at every call to
Database.store()
.By default it is set to False.
- Return type
None
- add_constraint(cstr_func, value=None, cstr_type=None, positive=False)[source]
Add a constraint (equality and inequality) to the optimization problem.
- Parameters
cstr_func (MDOFunction) – The constraint.
value (float | None) –
The value for which the constraint is active. If None, this value is 0.
By default it is set to None.
cstr_type (str | None) –
The type of the constraint. Either equality or inequality.
By default it is set to None.
positive (bool) –
If True, then the inequality constraint is positive.
By default it is set to False.
- Raises
TypeError – When the constraint of a linear optimization problem is not an
MDOLinearFunction
.ValueError – When the type of the constraint is missing.
- Return type
None
- add_eq_constraint(cstr_func, value=None)[source]
Add an equality constraint to the optimization problem.
- Parameters
cstr_func (MDOFunction) – The constraint.
value (float | None) –
The value for which the constraint is active. If None, this value is 0.
By default it is set to None.
- Return type
None
- add_ineq_constraint(cstr_func, value=None, positive=False)[source]
Add an inequality constraint to the optimization problem.
- Parameters
cstr_func (MDOFunction) – The constraint.
value (float | None) –
The value for which the constraint is active. If None, this value is 0.
By default it is set to None.
positive (bool) –
If True, then the inequality constraint is positive.
By default it is set to False.
- Return type
None
- add_observable(obs_func, new_iter=True)[source]
Add a function to be observed.
When the
OptimizationProblem
is executed, the observables are called following this sequence:The optimization algorithm calls the objective function with a normalized
x_vect
.The
OptimizationProblem.preprocess_functions()
wraps the function as aNormDBFunction
, which unnormalizes thex_vect
before evaluation.The unnormalized
x_vect
and the result of the evaluation are stored in theOptimizationProblem.database
.The previous step triggers the
OptimizationProblem.new_iter_listeners
, which calls the observables with the unnormalizedx_vect
.The observables themselves are wrapped as a
NormDBFunction
byOptimizationProblem.preprocess_functions()
, but in this case the input is always expected as unnormalized to avoid an additional normalizing-unnormalizing step.Finally, the output is stored in the
OptimizationProblem.database
.
- Parameters
obs_func (gemseo.core.mdofunctions.mdo_function.MDOFunction) – An observable to be observed.
new_iter (bool) –
If True, then the observable will be called at each new iterate.
By default it is set to True.
- Return type
None
- aggregate_constraint(constr_id, method='max', groups=None, **options)[source]
Aggregates a constraint to generate a reduced dimension constraint.
- Parameters
constr_id (int) – The index of the constraint in
constraints
.method (str | Callable[[Callable], Callable]) –
The aggregation method, e.g.
"max"
,"KS"
or"IKS"
.By default it is set to max.
groups (tuple[ndarray] | None) –
The groups for which to produce an output. If
None
, a single output constraint is produced.By default it is set to None.
**options (Any) – The options of the aggregation method.
- Raises
ValueError – When the given is index is greater or equal than the number of constraints or when the method is aggregation unknown.
- change_objective_sign()[source]
Change the objective function sign in order to minimize its opposite.
The
OptimizationProblem
expresses any optimization problem as a minimization problem. Then, an objective function originally expressed as a performance function to maximize must be converted into a cost function to minimize, by means of this method.- Return type
None
- check()[source]
Check if the optimization problem is ready for run.
- Raises
ValueError – If the objective function is missing.
- Return type
None
- static check_format(input_function)[source]
Check that a function is an instance of
MDOFunction
.- Parameters
input_function (Any) – The function to be tested.
- Raises
TypeError – If the function is not a
MDOFunction
.- Return type
None
- clear_listeners()[source]
Clear all the listeners.
- Return type
None
- evaluate_functions(x_vect=None, eval_jac=False, eval_obj=True, eval_observables=False, normalize=True, no_db_no_norm=False)[source]
Compute the functions of interest, and possibly their derivatives.
These functions of interest are the constraints, and possibly the objective.
Some optimization libraries require the number of constraints as an input parameter which is unknown by the formulation or the scenario. Evaluation of initial point allows to get this mandatory information. This is also used for design of experiments to evaluate samples.
- Parameters
x_vect (ndarray) –
The input vector at which the functions must be evaluated; if None, the initial point x_0 is used.
By default it is set to None.
eval_jac (bool) –
Whether to compute the Jacobian matrices of the functions of interest.
By default it is set to False.
eval_obj (bool) –
Whether to consider the objective function as a function of interest.
By default it is set to True.
normalize (bool) –
Whether to consider the input vector
x_vect
normalized.By default it is set to True.
no_db_no_norm (bool) –
If True, then do not use the pre-processed functions, so we have no database, nor normalization.
By default it is set to False.
eval_observables (bool) –
By default it is set to False.
- Returns
The output values of the functions of interest, as well as their Jacobian matrices if
eval_jac
isTrue
.- Return type
- execute_observables_callback(last_x)[source]
The callback function to be passed to the database.
Call all the observables with the last design variables values as argument.
- Parameters
last_x (numpy.ndarray) – The design variables values from the last evaluation.
- Return type
None
- export_hdf(file_path, append=False)[source]
Export the optimization problem to an HDF file.
- export_to_dataset(name=None, by_group=True, categorize=True, opt_naming=True, export_gradients=False, input_values=None)[source]
Export the database of the optimization problem to a
Dataset
.The variables can be classified into groups:
Dataset.DESIGN_GROUP
orDataset.INPUT_GROUP
for the design variables andDataset.FUNCTION_GROUP
orDataset.OUTPUT_GROUP
for the functions (objective, constraints and observables).- Parameters
name (str | None) –
The name to be given to the dataset. If
None
, use the name of theOptimizationProblem.database
.By default it is set to None.
by_group (bool) –
Whether to store the data by group in
Dataset.data
, in the sense of one unique NumPy array per group. Ifcategorize
isFalse
, there is a unique group:Dataset.PARAMETER_GROUP`
. Ifcategorize
isTrue
, the groups can be eitherDataset.DESIGN_GROUP
andDataset.FUNCTION_GROUP
ifopt_naming
isTrue
, orDataset.INPUT_GROUP
andDataset.OUTPUT_GROUP
. Ifby_group
isFalse
, store the data by variable names.By default it is set to True.
categorize (bool) –
Whether to distinguish between the different groups of variables. Otherwise, group all the variables in
Dataset.PARAMETER_GROUP`
.By default it is set to True.
opt_naming (bool) –
Whether to use
Dataset.DESIGN_GROUP
andDataset.FUNCTION_GROUP
as groups. Otherwise, useDataset.INPUT_GROUP
andDataset.OUTPUT_GROUP
.By default it is set to True.
export_gradients (bool) –
Whether to export the gradients of the functions (objective function, constraints and observables) if the latter are available in the database of the optimization problem.
By default it is set to False.
input_values (Iterable[ndarray] | None) –
The input values to be considered. If
None
, consider all the input values of the database.By default it is set to None.
- Returns
A dataset built from the database of the optimization problem.
- Return type
- get_active_ineq_constraints(x_vect, tol=1e-06)[source]
For each constraint, indicate if its different components are active.
- Parameters
x_vect (numpy.ndarray) – The vector of design variables.
tol (float) –
The tolerance for deciding whether a constraint is active.
By default it is set to 1e-06.
- Returns
For each constraint, a boolean indicator of activation of its different components.
- Return type
dict[gemseo.core.mdofunctions.mdo_function.MDOFunction, numpy.ndarray]
- get_all_functions()[source]
Retrieve all the functions of the optimization problem.
These functions are the constraints, the objective function and the observables.
- Returns
All the functions of the optimization problem.
- Return type
- get_all_functions_names()[source]
Retrieve the names of all the function of the optimization problem.
These functions are the constraints, the objective function and the observables.
- get_best_infeasible_point()[source]
Retrieve the best infeasible point within a given tolerance.
- Returns
The best infeasible point expressed as the design variables values, the objective function value, the feasibility of the point and the functions values.
- Return type
Tuple[Optional[numpy.ndarray], Optional[numpy.ndarray], bool, Dict[str, numpy.ndarray]]
- get_constraints_names()[source]
Retrieve the names of the constraints.
- get_constraints_number()[source]
Retrieve the number of constraints.
- Returns
The number of constraints.
- Return type
- get_data_by_names(names, as_dict=True, filter_non_feasible=False)[source]
Return the data for specific names of variables.
- Parameters
- Returns
The data related to the variables.
- Return type
- get_design_variable_names()[source]
Retrieve the names of the design variables.
- get_dimension()[source]
Retrieve the total number of design variables.
- Returns
The dimension of the design space.
- Return type
- get_eq_constraints()[source]
Retrieve all the equality constraints.
- Returns
The equality constraints.
- Return type
- get_eq_constraints_number()[source]
Retrieve the number of equality constraints.
- Returns
The number of equality constraints.
- Return type
- get_eq_cstr_total_dim()[source]
Retrieve the total dimension of the equality constraints.
This dimension is the sum of all the outputs dimensions of all the equality constraints.
- Returns
The total dimension of the equality constraints.
- Return type
- get_feasible_points()[source]
Retrieve the feasible points within a given tolerance.
This tolerance is defined by
OptimizationProblem.eq_tolerance
for equality constraints andOptimizationProblem.ineq_tolerance
for inequality ones.
- get_function_dimension(name)[source]
Return the dimension of a function of the problem (e.g. a constraint).
- Parameters
name (str) – The name of the function.
- Returns
The dimension of the function.
- Raises
ValueError – If the function name is unknown to the problem.
RuntimeError – If the function dimension is not unavailable.
- Return type
- get_function_names(names)[source]
Return the names of the functions stored in the database.
- get_functions_dimensions(names=None)[source]
Return the dimensions of the outputs of the problem functions.
- Parameters
names (Iterable[str] | None) –
The names of the functions. If None, then the objective and all the constraints are considered.
By default it is set to None.
- Returns
The dimensions of the outputs of the problem functions. The dictionary keys are the functions names and the values are the functions dimensions.
- Return type
- get_ineq_constraints()[source]
Retrieve all the inequality constraints.
- Returns
The inequality constraints.
- Return type
- get_ineq_constraints_number()[source]
Retrieve the number of inequality constraints.
- Returns
The number of inequality constraints.
- Return type
- get_ineq_cstr_total_dim()[source]
Retrieve the total dimension of the inequality constraints.
This dimension is the sum of all the outputs dimensions of all the inequality constraints.
- Returns
The total dimension of the inequality constraints.
- Return type
- get_nonproc_constraints()[source]
Retrieve the non-processed constraints.
- Returns
The non-processed constraints.
- Return type
- get_nonproc_objective()[source]
Retrieve the non-processed objective function.
- get_number_of_unsatisfied_constraints(design_variables)[source]
Return the number of scalar constraints not satisfied by design variables.
- Parameters
design_variables (numpy.ndarray) – The design variables.
- Returns
The number of unsatisfied scalar constraints.
- Return type
- get_objective_name(standardize=True)[source]
Retrieve the name of the objective function.
- get_observable(name)[source]
Retrieve an observable from its name.
- Parameters
name (str) – The name of the observable.
- Returns
The observable.
- Raises
ValueError – If the observable cannot be found.
- Return type
- get_optimum()[source]
Return the optimum solution within a given feasibility tolerances.
- Returns
The optimum result, defined by:
the value of the objective function,
the value of the design variables,
the indicator of feasibility of the optimal solution,
the value of the constraints,
the value of the gradients of the constraints.
- Return type
Tuple[numpy.ndarray, numpy.ndarray, bool, Dict[str, numpy.ndarray], Dict[str, numpy.ndarray]]
- get_scalar_constraints_names()[source]
Return the names of the scalar constraints.
- get_violation_criteria(x_vect)[source]
Compute a violation measure associated to an iteration.
For each constraint, when it is violated, add the absolute distance to zero, in L2 norm.
If 0, all constraints are satisfied
- Parameters
x_vect (numpy.ndarray) – The vector of the design variables values.
- Returns
The feasibility of the point and the violation measure.
- Return type
- get_x0_normalized(cast_to_real=False)[source]
Return the current values of the design variables after normalization.
- Parameters
cast_to_real (bool) –
Whether to cast the return value to real.
By default it is set to False.
- Returns
The current values of the design variables normalized between 0 and 1 from their lower and upper bounds.
- Return type
- has_constraints()[source]
Check if the problem has equality or inequality constraints.
- Returns
True if the problem has equality or inequality constraints.
- has_eq_constraints()[source]
Check if the problem has equality constraints.
- Returns
True if the problem has equality constraints.
- Return type
- has_ineq_constraints()[source]
Check if the problem has inequality constraints.
- Returns
True if the problem has inequality constraints.
- Return type
- has_nonlinear_constraints()[source]
Check if the problem has non-linear constraints.
- Returns
True if the problem has equality or inequality constraints.
- Return type
- classmethod import_hdf(file_path, x_tolerance=0.0)[source]
Import an optimization history from an HDF file.
- Parameters
- Returns
The read optimization problem.
- Return type
- is_max_iter_reached()[source]
Check if the maximum amount of iterations has been reached.
- Returns
Whether the maximum amount of iterations has been reached.
- Return type
- is_point_feasible(out_val, constraints=None)[source]
Check if a point is feasible.
Note
If the value of a constraint is absent from this point, then this constraint will be considered satisfied.
- Parameters
out_val (dict[str, ndarray]) – The values of the objective function, and eventually constraints.
constraints (Iterable[MDOFunction] | None) –
The constraints whose values are to be tested. If None, then take all constraints of the problem.
By default it is set to None.
- Returns
The feasibility of the point.
- Return type
- preprocess_functions(is_function_input_normalized=True, use_database=True, round_ints=True, eval_obs_jac=False)[source]
Pre-process all the functions and eventually the gradient.
Required to wrap the objective function and constraints with the database and eventually the gradients by complex step or finite differences.
- Parameters
is_function_input_normalized (bool) –
Whether to consider the function input as normalized and unnormalize it before the evaluation takes place.
By default it is set to True.
use_database (bool) –
Whether to wrap the functions in the database.
By default it is set to True.
round_ints (bool) –
Whether to round the integer variables.
By default it is set to True.
eval_obs_jac (bool) –
Whether to evaluate the Jacobian of the observables.
By default it is set to False.
- Return type
None
- static repr_constraint(func, ctype, value=None, positive=False)[source]
Express a constraint as a string expression.
- Parameters
func (MDOFunction) – The constraint function.
ctype (str) – The type of the constraint. Either equality or inequality.
value (float | None) –
The value for which the constraint is active. If None, this value is 0.
By default it is set to None.
positive (bool) –
If True, then the inequality constraint is positive.
By default it is set to False.
- Returns
A string representation of the constraint.
- Return type
- reset(database=True, current_iter=True, design_space=True, function_calls=True, preprocessing=True)[source]
Partially or fully reset the optimization problem.
- Parameters
database (bool) –
Whether to clear the database.
By default it is set to True.
current_iter (bool) –
Whether to reset the current iteration
OptimizationProblem.current_iter
.By default it is set to True.
design_space (bool) –
Whether to reset the current point of the
OptimizationProblem.design_space
to its initial value (possibly none).By default it is set to True.
function_calls (bool) –
Whether to reset the number of calls of the functions.
By default it is set to True.
preprocessing (bool) –
Whether to turn the pre-processing of functions to False.
By default it is set to True.
- Return type
None
- activate_bound_check: ClassVar[bool] = True
Whether to check if a point is in the design space before calling functions.
- constraint_names: dict[str, list[str]]
The standardized constraint names bound to the original ones.
- constraints: list[gemseo.core.mdofunctions.mdo_function.MDOFunction]
The constraints.
- database: gemseo.algos.database.Database
The database to store the optimization problem data.
- design_space: gemseo.algos.design_space.DesignSpace
The design space on which the optimization problem is solved.
- property differentiation_method: str
The differentiation method.
- property dimension: int
The dimension of the design space.
- eq_tolerance: float
The tolerance for the equality constraints.
- fd_step: float
The finite differences step.
- ineq_tolerance: float
The tolerance for the inequality constraints.
- property is_mono_objective: bool
Whether the optimization problem is mono-objective.
- minimize_objective: bool
Whether to maximize the objective.
- new_iter_observables: list[gemseo.core.mdofunctions.mdo_function.MDOFunction]
The observables to be called at each new iterate.
- nonproc_constraints: list[gemseo.core.mdofunctions.mdo_function.MDOFunction]
The non-processed constraints.
- nonproc_new_iter_observables: list[gemseo.core.mdofunctions.mdo_function.MDOFunction]
The non-processed observables to be called at each new iterate.
- nonproc_objective: gemseo.core.mdofunctions.mdo_function.MDOFunction
The non-processed objective function.
- nonproc_observables: list[gemseo.core.mdofunctions.mdo_function.MDOFunction]
The non-processed observables.
- property objective: gemseo.core.mdofunctions.mdo_function.MDOFunction
The objective function.
- observables: list[gemseo.core.mdofunctions.mdo_function.MDOFunction]
The observables.
- property parallel_differentiation: bool
Whether to approximate the derivatives in parallel.
- property parallel_differentiation_options: bool
The options to approximate the derivatives in parallel.
- pb_type: str
The type of optimization problem.
- preprocess_options: dict
The options to pre-process the functions.
- solution: gemseo.algos.opt_result.OptimizationResult
The solution of the optimization problem.
- stop_if_nan: bool
Whether the optimization stops when a function returns
NaN
.
- use_standardized_objective: bool
Whether to use standardized objective for logging and post-processing.
The standardized objective corresponds to the original one expressed as a cost function to minimize. A
DriverLib
works with this standardized objective and theDatabase
stores its values. However, for convenience, it may be more relevant to log the expression and the values of the original objective.
Driver library.
A driver library aims to solve an OptimizationProblem
using a particular algorithm from a particular family of numerical methods.
This algorithm will be in charge of evaluating the objective and constraints
functions at different points of the design space, using the
DriverLib.execute()
method.
The most famous kinds of numerical methods to solve an optimization problem
are optimization algorithms and design of experiments (DOE). A DOE driver
browses the design space agnostically, i.e. without taking into
account the function evaluations. On the contrary, an optimization algorithm
uses this information to make the journey through design space
as relevant as possible in order to reach as soon as possible the optimum.
These families are implemented in DOELibrary
and OptimizationLibrary
.
- class gemseo.algos.driver_lib.DriverDescription(algorithm_name, internal_algorithm_name, library_name='', description='', website='', handle_integer_variables=False, require_gradient=False)[source]
The description of a driver.
- Parameters
algorithm_name (str) –
internal_algorithm_name (str) –
library_name (str) –
By default it is set to .
description (str) –
By default it is set to .
website (str) –
By default it is set to .
handle_integer_variables (bool) –
By default it is set to False.
require_gradient (bool) –
By default it is set to False.
- Return type
None
- handle_integer_variables: bool = False
Whether the optimization algorithm handles integer variables.
- require_gradient: bool = False
Whether the optimization algorithm requires the gradient.
- class gemseo.algos.driver_lib.DriverLib[source]
Abstract class for DOE & optimization libraries interfaces.
Lists available methods in the library for the proposed problem to be solved.
To integrate an optimization package, inherit from this class and put your file in gemseo.algos.doe or gemseo.algo.opt packages.
- deactivate_progress_bar()[source]
Deactivate the progress bar.
- Return type
None
- driver_has_option(option_name)
Check the existence of an option.
- ensure_bounds(orig_func, normalize=True)[source]
Project the design vector onto the design space before execution.
- Parameters
orig_func – The original function.
normalize –
Whether to use the normalized design space.
By default it is set to True.
- Returns
A function calling the original function with the input data projected onto the design space.
- execute(problem, algo_name=None, eval_obs_jac=False, skip_int_check=False, **options)[source]
Execute the driver.
- Parameters
problem (OptimizationProblem) – The problem to be solved.
algo_name (str | None) –
The name of the algorithm. If None, use the algo_name attribute which may have been set by the factory.
By default it is set to None.
eval_obs_jac (bool) –
Whether to evaluate the Jacobian of the observables.
By default it is set to False.
skip_int_check (bool) –
Whether to skip the integer variable handling check of the selected algorithm.
By default it is set to False.
**options (DriverLibOptionType) – The options for the algorithm.
- Returns
The optimization result.
- Raises
ValueError – If algo_name was not either set by the factory or given as an argument.
- Return type
- filter_adapted_algorithms(problem)
Filter the algorithms capable of solving the problem.
- finalize_iter_observer()[source]
Finalize the iteration observer.
- Return type
None
- get_optimum_from_database(message=None, status=None)[source]
Retrieves the optimum from the database and builds an optimization result object from it.
- get_x0_and_bounds_vects(normalize_ds)[source]
Gets x0, bounds, normalized or not depending on algo options, all as numpy arrays.
- Parameters
normalize_ds – Whether to normalize the input variables that are not integers, according to the normalization policy of the design space.
- Returns
The current value, the lower bounds and the upper bounds.
- init_iter_observer(max_iter, message)[source]
Initialize the iteration observer.
It will handle the stopping criterion and the logging of the progress bar.
- Parameters
- Raises
ValueError – If the max_iter is not greater than or equal to one.
- Return type
None
- init_options_grammar(algo_name)
Initialize the options grammar.
- Parameters
algo_name (str) – The name of the algorithm.
- Return type
- is_algo_requires_grad(algo_name)[source]
Returns True if the algorithm requires a gradient evaluation.
- Parameters
algo_name – The name of the algorithm.
- static is_algorithm_suited(algorithm_description, problem)
Check if the algorithm is suited to the problem according to its description.
- Parameters
algorithm_description (gemseo.algos.algo_lib.AlgorithmDescription) – The description of the algorithm.
problem (Any) – The problem to be solved.
- Returns
Whether the algorithm is suited to the problem.
- Return type
- new_iteration_callback(x_vect=None)[source]
Callback called at each new iteration, i.e. every time a design vector that is not already in the database is proposed by the optimizer.
Iterate the progress bar, implement the stop criteria.
- Parameters
x_vect (ndarray | None) –
The design variables values. If None, use the values of the last iteration.
By default it is set to None.
- Raises
MaxTimeReached – If the elapsed time is greater than the maximum execution time.
- Return type
None
- activate_progress_bar: ClassVar[bool] = True
Whether to activate the progress bar in the optimization log.
- class gemseo.algos.driver_lib.ProgressBar(*_, **__)[source]
Extend tqdm progress bar with better time units.
Use hour, day or week for slower processes.
- Parameters
iterable (iterable, optional) – Iterable to decorate with a progressbar. Leave blank to manually manage the updates.
desc (str, optional) – Prefix for the progressbar.
total (int or float, optional) – The number of expected iterations. If unspecified, len(iterable) is used if possible. If float(“inf”) or as a last resort, only basic progress statistics are displayed (no ETA, no progressbar). If gui is True and this parameter needs subsequent updating, specify an initial arbitrary large positive number, e.g. 9e9.
leave (bool, optional) – If [default: True], keeps all traces of the progressbar upon termination of iteration. If None, will leave only if position is 0.
file (io.TextIOWrapper or io.StringIO, optional) – Specifies where to output the progress messages (default: sys.stderr). Uses file.write(str) and file.flush() methods. For encoding, see write_bytes.
ncols (int, optional) – The width of the entire output message. If specified, dynamically resizes the progressbar to stay within this bound. If unspecified, attempts to use environment width. The fallback is a meter width of 10 and no limit for the counter and statistics. If 0, will not print any meter (only stats).
mininterval (float, optional) – Minimum progress display update interval [default: 0.1] seconds.
maxinterval (float, optional) – Maximum progress display update interval [default: 10] seconds. Automatically adjusts miniters to correspond to mininterval after long display update lag. Only works if dynamic_miniters or monitor thread is enabled.
miniters (int or float, optional) – Minimum progress display update interval, in iterations. If 0 and dynamic_miniters, will automatically adjust to equal mininterval (more CPU efficient, good for tight loops). If > 0, will skip display of specified number of iterations. Tweak this and mininterval to get very efficient loops. If your progress is erratic with both fast and slow iterations (network, skipping items, etc) you should set miniters=1.
ascii (bool or str, optional) – If unspecified or False, use unicode (smooth blocks) to fill the meter. The fallback is to use ASCII characters ” 123456789#”.
disable (bool, optional) – Whether to disable the entire progressbar wrapper [default: False]. If set to None, disable on non-TTY.
unit (str, optional) – String that will be used to define the unit of each iteration [default: it].
unit_scale (bool or int or float, optional) – If 1 or True, the number of iterations will be reduced/scaled automatically and a metric prefix following the International System of Units standard will be added (kilo, mega, etc.) [default: False]. If any other non-zero number, will scale total and n.
dynamic_ncols (bool, optional) – If set, constantly alters ncols and nrows to the environment (allowing for window resizes) [default: False].
smoothing (float, optional) – Exponential moving average smoothing factor for speed estimates (ignored in GUI mode). Ranges from 0 (average speed) to 1 (current/instantaneous speed) [default: 0.3].
bar_format (str, optional) –
Specify a custom bar string formatting. May impact performance. [default: ‘{l_bar}{bar}{r_bar}’], where l_bar=’{desc}: {percentage:3.0f}%|’ and r_bar=’| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, ‘
’{rate_fmt}{postfix}]’
- Possible vars: l_bar, bar, r_bar, n, n_fmt, total, total_fmt,
percentage, elapsed, elapsed_s, ncols, nrows, desc, unit, rate, rate_fmt, rate_noinv, rate_noinv_fmt, rate_inv, rate_inv_fmt, postfix, unit_divisor, remaining, remaining_s, eta.
Note that a trailing “: ” is automatically removed after {desc} if the latter is empty.
initial (int or float, optional) – The initial counter value. Useful when restarting a progress bar [default: 0]. If using float, consider specifying {n:.3f} or similar in bar_format, or specifying unit_scale.
position (int, optional) – Specify the line offset to print this bar (starting from 0) Automatic if unspecified. Useful to manage multiple bars at once (eg, from threads).
postfix (dict or *, optional) – Specify additional stats to display at the end of the bar. Calls set_postfix(**postfix) if possible (dict).
unit_divisor (float, optional) – [default: 1000], ignored unless unit_scale is True.
write_bytes (bool, optional) – If (default: None) and file is unspecified, bytes will be written in Python 2. If True will also write bytes. In all other cases will default to unicode.
lock_args (tuple, optional) – Passed to refresh for intermediate output (initialisation, iterating, and updating).
nrows (int, optional) – The screen height. If specified, hides nested bars outside this bound. If unspecified, attempts to use environment height. The fallback is 20.
colour (str, optional) – Bar colour (e.g. ‘green’, ‘#00ff00’).
delay (float, optional) – Don’t display until [default: 0] seconds have elapsed.
gui (bool, optional) – WARNING: internal parameter - do not use. Use tqdm.gui.tqdm(…) instead. If set, will attempt to use matplotlib animations for a graphical output [default: False].
- Returns
out
- Return type
decorated iterator.
- clear(nolock=False)
Clear current bar display.
- close()
Cleanup and (if leave=False) close the progressbar.
- display(msg=None, pos=None)
Use self.sp to display msg in the specified pos.
Consider overloading this function when inheriting to use e.g.: self.some_frontend(**self.format_dict) instead of self.sp.
- Parameters
msg (str, optional. What to display (default: repr(self)).) – By default it is set to None.
pos (int, optional. Position to moveto) –
(default: abs(self.pos)).
By default it is set to None.
- classmethod external_write_mode(file=None, nolock=False)
Disable tqdm within context and refresh tqdm when exits. Useful when writing to standard output stream
- static format_interval(t)
Formats a number of seconds as a clock time, [H:]MM:SS
- classmethod format_meter(n, total, elapsed, **kwargs)[source]
Return a string-based progress bar given some parameters
- Parameters
total (int or float) – The expected total number of iterations. If meaningless (None), only basic progress statistics are displayed (no ETA).
elapsed (float) – Number of seconds passed since start.
ncols (int, optional) – The width of the entire output message. If specified, dynamically resizes {bar} to stay within this bound [default: None]. If 0, will not print any bar (only stats). The fallback is {bar:10}.
prefix (str, optional) – Prefix message (included in total width) [default: ‘’]. Use as {desc} in bar_format string.
ascii (bool, optional or str, optional) – If not set, use unicode (smooth blocks) to fill the meter [default: False]. The fallback is to use ASCII characters ” 123456789#”.
unit (str, optional) – The iteration unit [default: ‘it’].
unit_scale (bool or int or float, optional) – If 1 or True, the number of iterations will be printed with an appropriate SI metric prefix (k = 10^3, M = 10^6, etc.) [default: False]. If any other non-zero number, will scale total and n.
rate (float, optional) – Manual override for iteration rate. If [default: None], uses n/elapsed.
bar_format (str, optional) –
Specify a custom bar string formatting. May impact performance. [default: ‘{l_bar}{bar}{r_bar}’], where l_bar=’{desc}: {percentage:3.0f}%|’ and r_bar=’| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, ‘
’{rate_fmt}{postfix}]’
- Possible vars: l_bar, bar, r_bar, n, n_fmt, total, total_fmt,
percentage, elapsed, elapsed_s, ncols, nrows, desc, unit, rate, rate_fmt, rate_noinv, rate_noinv_fmt, rate_inv, rate_inv_fmt, postfix, unit_divisor, remaining, remaining_s, eta.
Note that a trailing “: ” is automatically removed after {desc} if the latter is empty.
postfix (*, optional) – Similar to prefix, but placed at the end (e.g. for additional stats). Note: postfix is usually a string (not a dict) for this method, and will if possible be set to postfix = ‘, ‘ + postfix. However other types are supported (#382).
unit_divisor (float, optional) – [default: 1000], ignored unless unit_scale is True.
initial (int or float, optional) – The initial counter value [default: 0].
colour (str, optional) – Bar colour (e.g. ‘green’, ‘#00ff00’).
- Returns
out
- Return type
Formatted meter and stats, ready to display.
- static format_num(n)
Intelligent scientific notation (.3g).
- static format_sizeof(num, suffix='', divisor=1000)
Formats a number (greater than unity) with SI Order of Magnitude prefixes.
- Parameters
- Returns
out – Number with Order of Magnitude SI unit postfix.
- Return type
- classmethod get_lock()
Get the global lock. Construct it if it does not exist.
- classmethod pandas(**tqdm_kwargs)
- Registers the current tqdm class with
pandas.core. ( frame.DataFrame | series.Series | groupby.(generic.)DataFrameGroupBy | groupby.(generic.)SeriesGroupBy ).progress_apply
A new instance will be created every time progress_apply is called, and each instance will automatically close() upon completion.
- Parameters
tqdm_kwargs (arguments for the tqdm instance) –
Examples
>>> import pandas as pd >>> import numpy as np >>> from tqdm import tqdm >>> from tqdm.gui import tqdm as tqdm_gui >>> >>> df = pd.DataFrame(np.random.randint(0, 100, (100000, 6))) >>> tqdm.pandas(ncols=50) # can use tqdm_gui, optional kwargs, etc >>> # Now you can use `progress_apply` instead of `apply` >>> df.groupby(0).progress_apply(lambda x: x**2)
References
<https://stackoverflow.com/questions/18603270/ progress-indicator-during-pandas-operations-python>
- refresh(nolock=False, lock_args=None)
Force refresh the display of this bar.
- Parameters
- reset(total=None)
Resets to 0 iterations for repeated use.
Consider combining with leave=True.
- set_description(desc=None, refresh=True)
Set/modify description of the progress bar.
- set_description_str(desc=None, refresh=True)
Set/modify description without ‘: ‘ appended.
- classmethod set_lock(lock)
Set the global lock.
- set_postfix(ordered_dict=None, refresh=True, **kwargs)
Set/modify postfix (additional stats) with automatic formatting based on datatype.
- set_postfix_str(s='', refresh=True)
Postfix without dictionary expansion, similar to prefix handling.
- status_printer(file)[source]
Overload the status_printer method to avoid the use of closures.
- Parameters
file (io.TextIOWrapper | io.StringIO) – Specifies where to output the progress messages.
- Returns
The function to print the status in the progress bar.
- Return type
Callable[[str], None]
- unpause()
Restart tqdm timer from last print time.
- update(n=1)
Manually update the progress bar, useful for streams such as reading files. E.g.: >>> t = tqdm(total=filesize) # Initialise >>> for current_buffer in stream: … … … t.update(len(current_buffer)) >>> t.close() The last line is highly recommended, but possibly not necessary if t.update() will be called in such a way that filesize will be exactly reached and printed.
- Parameters
Increment to add to the internal counter of iterations [default: 1]. If using float, consider specifying {n:.3f} or similar in bar_format, or specifying unit_scale.
By default it is set to 1.
- Returns
out – True if a display() was triggered.
- Return type
bool or None
- classmethod wrapattr(stream, method, total=None, bytes=True, **tqdm_kwargs)
stream : file-like object. method : str, “read” or “write”. The result of read() and
the first argument of write() should have a len().
>>> with tqdm.wrapattr(file_obj, "read", total=file_obj.size) as fobj: ... while True: ... chunk = fobj.read(chunk_size) ... if not chunk: ... break
- classmethod write(s, file=None, end='\n', nolock=False)
Print a message via tqdm (without overlap with bars).
- property format_dict
Public API for read-only member access.
- class gemseo.algos.driver_lib.TqdmToLogger(initial_value='', newline='\n')[source]
Redirect tqdm output to the gemseo logger.
- close()
Close the IO object.
Attempting any further operation after the object is closed will raise a ValueError.
This method has no effect if the file is already closed.
- detach()
Separate the underlying buffer from the TextIOBase and return it.
After the underlying buffer has been detached, the TextIO is in an unusable state.
- fileno()
Returns underlying file descriptor if one exists.
OSError is raised if the IO object does not use a file descriptor.
- flush()
Flush write buffers, if applicable.
This is not implemented for read-only and non-blocking streams.
- getvalue()
Retrieve the entire contents of the object.
- isatty()
Return whether this is an ‘interactive’ stream.
Return False if it can’t be determined.
- read(size=- 1, /)
Read at most size characters, returned as a string.
If the argument is negative or omitted, read until EOF is reached. Return an empty string at EOF.
- readable()
Returns True if the IO object can be read.
- readline(size=- 1, /)
Read until newline or EOF.
Returns an empty string if EOF is hit immediately.
- readlines(hint=- 1, /)
Return a list of lines from the stream.
hint can be specified to control the number of lines read: no more lines will be read if the total size (in bytes/characters) of all lines so far exceeds hint.
- seek(pos, whence=0, /)
Change stream position.
- Seek to character offset pos relative to position indicated by whence:
0 Start of stream (the default). pos should be >= 0; 1 Current position - pos must be 0; 2 End of stream - pos must be 0.
Returns the new absolute position.
- seekable()
Returns True if the IO object can be seeked.
- tell()
Tell the current file position.
- truncate(pos=None, /)
Truncate size to pos.
The pos argument defaults to the current file position, as returned by tell(). The current file position is unchanged. Returns the new absolute position.
- writable()
Returns True if the IO object can be written.
- write(buf)[source]
Write buffer.
- writelines(lines, /)
Write a list of lines to stream.
Line separators are not added, so it is usual for each of the lines provided to have a line separator at the end.
- encoding
Encoding of the text stream.
Subclasses should override.
- errors
The error setting of the decoder or encoder.
Subclasses should override.
- newlines