calibration module¶
Calibration of a machine learning algorithm.
A machine learning algorithm depends on hyper-parameters, e.g. the number of clusters for a clustering algorithm, the regularization constant for a regression model, the kernel for a Gaussian process regression, … Its ability to generalize the information learned during the training stage, and thus to avoid over-fitting, which is an over-reliance on the learning data set, depends on the values of these hyper-parameters. Thus, the hyper-parameters minimizing the learning quality measure are rarely those minimizing the generalization one. Classically, the generalization one decreases before growing again as the model becomes more complex, while the learning error keeps decreasing. This phenomenon is called the curse of dimensionality.
In this module,
the MLAlgoCalibration
class aims to calibrate the hyper-parameters
in order to minimize this measure of the generalization quality
over a calibration parameter space.
This class relies on the MLAlgoAssessor
class
which is a discipline (MDODiscipline
)
built from a machine learning algorithm (MLAlgo
),
a dataset (Dataset
),
a quality measure (MLQualityMeasure
)
and various options for the data scaling,
the quality measure
and the machine learning algorithm.
The inputs of this discipline are hyper-parameters of the machine learning algorithm
while the output is the quality criterion.
Classes:
|
Discipline assessing the quality of a machine learning algorithm. |
|
Calibration of a machine learning algorithm. |
- class gemseo.mlearning.core.calibration.MLAlgoAssessor(algo, dataset, parameters, measure, measure_options=None, transformer=None, **algo_options)[source]¶
Bases:
gemseo.core.discipline.MDODiscipline
Discipline assessing the quality of a machine learning algorithm.
This quality depends on the values of parameters to calibrate with the
MLAlgoCalibration
.- input_grammar¶
The input grammar.
- Type
- output_grammar¶
The output grammar.
- Type
- grammar_type¶
The type of grammar to be used for inputs and outputs declaration.
- Type
str
- comp_dir¶
The path to the directory of the discipline module file if any.
- Type
str
- data_processor¶
A tool to pre- and post-process discipline data.
- Type
- re_exec_policy¶
The policy to re-execute the same discipline.
- Type
str
- residual_variables¶
The output variables to be considered as residuals; they shall be equal to zero.
- Type
List[str]
- jac¶
The Jacobians of the outputs wrt inputs of the form
{output: {input: matrix}}
.- Type
Dict[str, Dict[str, ndarray]]
- exec_for_lin¶
Whether the last execution was due to a linearization.
- Type
bool
- name¶
The name of the discipline.
- Type
str
- cache¶
The cache containing one or several executions of the discipline according to the cache policy.
- Type
- local_data¶
The last input and output data.
- Type
Dict[str, Any]
- algo¶
The name of a machine learning algorithm.
- Type
str
- measure¶
The measure to assess the machine learning algorithm.
- Type
- parameters¶
The parameters of the machine learning algorithm to calibrate.
- Type
List(str)
- transformer¶
The transformation strategy for data groups.
- Type
TransformerType
- algos¶
The instances of the machine learning algorithm (one per execution of the machine learning algorithm assessor).
- Type
List(MLAlgo)
Initialize self. See help(type(self)) for accurate signature.
- Parameters
algo (str) – The name of a machine learning algorithm.
dataset (Dataset) – A learning dataset.
parameters (Iterable[str]) – The parameters of the machine learning algorithm to calibrate.
measure (MLQualityMeasure) – A measure to assess the machine learning algorithm.
measure_options (Optional[MeasureOptionsType]) –
The options of the quality measure. If “multioutput” is missing, it is added with False as value. If None, do not use quality measure options.
By default it is set to None.
transformer (Dict[str,Transformer]) –
The strategies to transform the variables. The values are instances of
Transformer
while the keys are the names of either the variables or the groups of variables, e.g. “inputs” or “outputs” in the case of the regression algorithms. If a group is specified, theTransformer
will be applied to all the variables of this group. If None, do not transform the variables.By default it is set to None.
**algo_options (MLAlgoParameterType) –
- Raises
ValueError – If the measure option “multioutput” is True.
- Return type
None
Attributes:
The cache input tolerance.
The default inputs.
The cumulated execution time of the discipline.
The grammar type.
The linearization mode among
LINEARIZE_MODE_LIST
.The number of times the discipline was executed.
The number of times the discipline was linearized.
The status of the discipline.
Methods:
Activate the time stamps.
add_differentiated_inputs
([inputs])Add inputs against which to differentiate the outputs.
add_differentiated_outputs
([outputs])Add outputs to be differentiated.
add_status_observer
(obs)Add an observer for the status.
auto_get_grammar_file
([is_input, name, comp_dir])Use a naming convention to associate a grammar file to a discipline.
check_input_data
(input_data[, raise_exception])Check the input data validity.
check_jacobian
([input_data, derr_approx, ...])Check if the analytical Jacobian is correct with respect to a reference one.
check_output_data
([raise_exception])Check the output data validity.
Deactivate the time stamps.
deserialize
(in_file)Deserialize a discipline from a file.
execute
([input_data])Execute the discipline.
Return the local input data as a list.
Return the local output data as a list.
Define the names of the attributes to be serialized.
get_data_list_from_dict
(keys, data_dict)Filter the dict from a list of keys or a single key.
Return the expected data exchange sequence.
Return the expected execution sequence.
Return the local input data as a dictionary.
Return the names of the input variables.
Return the names of the input and output variables.
Return the local output data as a large NumPy array.
get_inputs_by_name
(data_names)Return the local data associated with input variables.
get_local_data_by_name
(data_names)Return the local data of the discipline associated with variables names.
Return the local output data as a dictionary.
Return the names of the output variables.
Return the local input data as a large NumPy array.
get_outputs_by_name
(data_names)Return the local data associated with output variables.
Return the sub-disciplines if any.
is_all_inputs_existing
(data_names)Test if several variables are discipline inputs.
is_all_outputs_existing
(data_names)Test if several variables are discipline outputs.
is_input_existing
(data_name)Test if a variable is a discipline input.
is_output_existing
(data_name)Test if a variable is a discipline output.
Whether the discipline is a scenario.
linearize
([input_data, force_all, force_no_exec])Execute the linearized version of the code.
Notify all status observers that the status has changed.
Remove an observer for the status.
Set all the statuses to
PENDING
.serialize
(out_file)Serialize the discipline and store it in a file.
set_cache_policy
([cache_type, ...])Set the type of cache to use and the tolerance level.
set_disciplines_statuses
(status)Set the sub-disciplines statuses.
set_jacobian_approximation
([...])Set the Jacobian approximation method.
set_optimal_fd_step
([outputs, inputs, ...])Compute the optimal finite-difference step.
store_local_data
(**kwargs)Store discipline data in local data.
- APPROX_MODES = ['finite_differences', 'complex_step']¶
- AVAILABLE_MODES = ('auto', 'direct', 'adjoint', 'reverse', 'finite_differences', 'complex_step')¶
- COMPLEX_STEP = 'complex_step'¶
- CRITERION = 'criterion'¶
- FINITE_DIFFERENCES = 'finite_differences'¶
- HDF5_CACHE = 'HDF5Cache'¶
- JSON_GRAMMAR_TYPE = 'JSONGrammar'¶
- LEARNING = 'learning'¶
- MEMORY_FULL_CACHE = 'MemoryFullCache'¶
- MULTIOUTPUT = 'multioutput'¶
- N_CPUS = 2¶
- RE_EXECUTE_DONE_POLICY = 'RE_EXEC_DONE'¶
- RE_EXECUTE_NEVER_POLICY = 'RE_EXEC_NEVER'¶
- SIMPLE_CACHE = 'SimpleCache'¶
- SIMPLE_GRAMMAR_TYPE = 'SimpleGrammar'¶
- STATUS_DONE = 'DONE'¶
- STATUS_FAILED = 'FAILED'¶
- STATUS_PENDING = 'PENDING'¶
- STATUS_RUNNING = 'RUNNING'¶
- STATUS_VIRTUAL = 'VIRTUAL'¶
- classmethod activate_time_stamps()¶
Activate the time stamps.
For storing start and end times of execution and linearizations.
- Return type
None
- add_differentiated_inputs(inputs=None)¶
Add inputs against which to differentiate the outputs.
This method updates
_differentiated_inputs
withinputs
.- Parameters
inputs (Optional[Iterable[str]]) –
The input variables against which to differentiate the outputs. If None, all the inputs of the discipline are used.
By default it is set to None.
- Raises
ValueError – When the inputs wrt which differentiate the discipline are not inputs of the latter.
- Return type
None
- add_differentiated_outputs(outputs=None)¶
Add outputs to be differentiated.
This method updates
_differentiated_outputs
withoutputs
.- Parameters
outputs (Optional[Iterable[str]]) –
The output variables to be differentiated. If None, all the outputs of the discipline are used.
By default it is set to None.
- Raises
ValueError – When the outputs to differentiate are not discipline outputs.
- Return type
None
- add_status_observer(obs)¶
Add an observer for the status.
Add an observer for the status to be notified when self changes of status.
- Parameters
obs (Any) – The observer to add.
- Return type
None
- auto_get_grammar_file(is_input=True, name=None, comp_dir=None)¶
Use a naming convention to associate a grammar file to a discipline.
This method searches in a directory for either an input grammar file named
name + "_input.json"
or an output grammar file named``name + “_output.json”``.- Parameters
is_input (bool) –
If True, autodetect the input grammar file; otherwise, autodetect the output grammar file.
By default it is set to True.
name (Optional[str]) –
The name to be searched in the file names. If None, use the
name
name of the discipline.By default it is set to None.
comp_dir (Optional[Union[str, pathlib.Path]]) –
The directory in which to search the grammar file. If None, use
comp_dir
.By default it is set to None.
- Returns
The grammar file path.
- Return type
pathlib.Path
- property cache_tol¶
The cache input tolerance.
This is the tolerance for equality of the inputs in the cache. If norm(stored_input_data-input_data) <= cache_tol * norm(stored_input_data), the cached data for
stored_input_data
is returned when callingself.execute(input_data)
.
- check_input_data(input_data, raise_exception=True)¶
Check the input data validity.
- Parameters
input_data (Dict[str, Any]) – The input data needed to execute the discipline according to the discipline input grammar.
raise_exception (bool) –
By default it is set to True.
- Return type
None
- check_jacobian(input_data=None, derr_approx='finite_differences', step=1e-07, threshold=1e-08, linearization_mode='auto', inputs=None, outputs=None, parallel=False, n_processes=2, use_threading=False, wait_time_between_fork=0, auto_set_step=False, plot_result=False, file_path='jacobian_errors.pdf', show=False, figsize_x=10, figsize_y=10, reference_jacobian_path=None, save_reference_jacobian=False, indices=None)¶
Check if the analytical Jacobian is correct with respect to a reference one.
If reference_jacobian_path is not None and save_reference_jacobian is True, compute the reference Jacobian with the approximation method and save it in reference_jacobian_path.
If reference_jacobian_path is not None and save_reference_jacobian is False, do not compute the reference Jacobian but read it from reference_jacobian_path.
If reference_jacobian_path is None, compute the reference Jacobian without saving it.
- Parameters
input_data –
The input data needed to execute the discipline according to the discipline input grammar. If None, use the
default_inputs
.By default it is set to None.
derr_approx –
The approximation method, either “complex_step” or “finite_differences”.
By default it is set to finite_differences.
threshold –
The acceptance threshold for the Jacobian error.
By default it is set to 1e-08.
linearization_mode –
the mode of linearization: direct, adjoint or automated switch depending on dimensions of inputs and outputs (Default value = ‘auto’)
By default it is set to auto.
inputs –
The names of the inputs wrt which to differentiate the outputs.
By default it is set to None.
outputs –
The names of the outputs to be differentiated.
By default it is set to None.
step –
The differentiation step.
By default it is set to 1e-07.
parallel –
Whether to differentiate the discipline in parallel.
By default it is set to False.
n_processes –
The maximum number of processors on which to run.
By default it is set to 2.
use_threading –
Whether to use threads instead of processes to parallelize the execution; multiprocessing will copy (serialize) all the disciplines, while threading will share all the memory This is important to note if you want to execute the same discipline multiple times, you shall use multiprocessing.
By default it is set to False.
wait_time_between_fork –
The time waited between two forks of the process / thread.
By default it is set to 0.
auto_set_step –
Whether to compute the optimal step for a forward first order finite differences gradient approximation.
By default it is set to False.
plot_result –
Whether to plot the result of the validation (computed vs approximated Jacobians).
By default it is set to False.
file_path –
The path to the output file if
plot_result
isTrue
.By default it is set to jacobian_errors.pdf.
show –
Whether to open the figure.
By default it is set to False.
figsize_x –
The x-size of the figure in inches.
By default it is set to 10.
figsize_y –
The y-size of the figure in inches.
By default it is set to 10.
reference_jacobian_path –
The path of the reference Jacobian file.
By default it is set to None.
save_reference_jacobian –
Whether to save the reference Jacobian.
By default it is set to False.
indices –
The indices of the inputs and outputs for the different sub-Jacobian matrices, formatted as
{variable_name: variable_components}
wherevariable_components
can be either an integer, e.g. 2 a sequence of integers, e.g. [0, 3], a slice, e.g. slice(0,3), the ellipsis symbol (…) or None, which is the same as ellipsis. If a variable name is missing, consider all its components. If None, consider all the components of all theinputs
andoutputs
.By default it is set to None.
- Returns
Whether the analytical Jacobian is correct with respect to the reference one.
- check_output_data(raise_exception=True)¶
Check the output data validity.
- Parameters
raise_exception (bool) –
Whether to raise an exception when the data is invalid.
By default it is set to True.
- Return type
None
- classmethod deactivate_time_stamps()¶
Deactivate the time stamps.
For storing start and end times of execution and linearizations.
- Return type
None
- property default_inputs¶
The default inputs.
- Raises
TypeError – When the default inputs are not passed as a dictionary.
- static deserialize(in_file)¶
Deserialize a discipline from a file.
- Parameters
in_file (Union[str, pathlib.Path]) – The path to the file containing the discipline.
- Returns
The discipline instance.
- Return type
- property exec_time¶
The cumulated execution time of the discipline.
Note
This property is multiprocessing safe.
- execute(input_data=None)¶
Execute the discipline.
This method executes the discipline:
Adds the default inputs to the
input_data
if some inputs are not defined in input_data but exist in_default_inputs
.Checks whether the last execution of the discipline was called with identical inputs, ie. cached in
cache
; if so, directly returnsself.cache.get_output_cache(inputs)
.Caches the inputs.
Checks the input data against
input_grammar
.If
data_processor
is not None, runs the preprocessor.Updates the status to
RUNNING
.Calls the
_run()
method, that shall be defined.If
data_processor
is not None, runs the postprocessor.Checks the output data.
Caches the outputs.
Updates the status to
DONE
orFAILED
.Updates summed execution time.
- Parameters
input_data (Optional[Dict[str, Any]]) –
The input data needed to execute the discipline according to the discipline input grammar. If None, use the
default_inputs
.By default it is set to None.
- Returns
The discipline local data after execution.
- Return type
Dict[str, Any]
- get_all_inputs()¶
Return the local input data as a list.
The order is given by
get_input_data_names()
.- Returns
The local input data.
- Return type
List[Any]
- get_all_outputs()¶
Return the local output data as a list.
The order is given by
get_output_data_names()
.- Returns
The local output data.
- Return type
List[Any]
- get_attributes_to_serialize()¶
Define the names of the attributes to be serialized.
Shall be overloaded by disciplines
- Returns
The names of the attributes to be serialized.
- static get_data_list_from_dict(keys, data_dict)¶
Filter the dict from a list of keys or a single key.
If keys is a string, then the method return the value associated to the key. If keys is a list of strings, then the method returns a generator of value corresponding to the keys which can be iterated.
- Parameters
keys (Union[str, Iterable]) – One or several names.
data_dict (Dict[str, Any]) – The mapping from which to get the data.
- Returns
Either a data or a generator of data.
- Return type
Union[Any, Generator[Any]]
- get_expected_dataflow()¶
Return the expected data exchange sequence.
This method is used for the XDSM representation.
The default expected data exchange sequence is an empty list.
See also
MDOFormulation.get_expected_dataflow
- Returns
The data exchange arcs.
- Return type
List[Tuple[gemseo.core.discipline.MDODiscipline, gemseo.core.discipline.MDODiscipline, List[str]]]
- get_expected_workflow()¶
Return the expected execution sequence.
This method is used for the XDSM representation.
The default expected execution sequence is the execution of the discipline itself.
See also
MDOFormulation.get_expected_workflow
- Returns
The expected execution sequence.
- Return type
- get_input_data()¶
Return the local input data as a dictionary.
- Returns
The local input data.
- Return type
Dict[str, Any]
- get_input_data_names()¶
Return the names of the input variables.
- Returns
The names of the input variables.
- Return type
List[str]
- get_input_output_data_names()¶
Return the names of the input and output variables.
- Returns
The name of the input and output variables.
- Return type
List[str]
- get_inputs_asarray()¶
Return the local output data as a large NumPy array.
The order is the one of
get_all_outputs()
.- Returns
The local output data.
- Return type
numpy.ndarray
- get_inputs_by_name(data_names)¶
Return the local data associated with input variables.
- Parameters
data_names (Iterable[str]) – The names of the input variables.
- Returns
The local data for the given input variables.
- Raises
ValueError – When a variable is not an input of the discipline.
- Return type
List[Any]
- get_local_data_by_name(data_names)¶
Return the local data of the discipline associated with variables names.
- Parameters
data_names (Iterable[str]) – The names of the variables.
- Returns
The local data associated with the variables names.
- Raises
ValueError – When a name is not not a discipline input name.
- Return type
Generator[Any]
- get_output_data()¶
Return the local output data as a dictionary.
- Returns
The local output data.
- Return type
Dict[str, Any]
- get_output_data_names()¶
Return the names of the output variables.
- Returns
The names of the output variables.
- Return type
List[str]
- get_outputs_asarray()¶
Return the local input data as a large NumPy array.
The order is the one of
get_all_inputs()
.- Returns
The local input data.
- Return type
numpy.ndarray
- get_outputs_by_name(data_names)¶
Return the local data associated with output variables.
- Parameters
data_names (Iterable[str]) – The names of the output variables.
- Returns
The local data for the given output variables.
- Raises
ValueError – When a variable is not an output of the discipline.
- Return type
List[Any]
- get_sub_disciplines()¶
Return the sub-disciplines if any.
- Returns
The sub-disciplines.
- Return type
- property grammar_type¶
The grammar type.
- is_all_inputs_existing(data_names)¶
Test if several variables are discipline inputs.
- Parameters
data_names (Iterable[str]) – The names of the variables.
- Returns
Whether all the variables are discipline inputs.
- Return type
bool
- is_all_outputs_existing(data_names)¶
Test if several variables are discipline outputs.
- Parameters
data_names (Iterable[str]) – The names of the variables.
- Returns
Whether all the variables are discipline outputs.
- Return type
bool
- is_input_existing(data_name)¶
Test if a variable is a discipline input.
- Parameters
data_name (str) – The name of the variable.
- Returns
Whether the variable is a discipline input.
- Return type
bool
- is_output_existing(data_name)¶
Test if a variable is a discipline output.
- Parameters
data_name (str) – The name of the variable.
- Returns
Whether the variable is a discipline output.
- Return type
bool
- static is_scenario()¶
Whether the discipline is a scenario.
- Return type
bool
- property linearization_mode¶
The linearization mode among
LINEARIZE_MODE_LIST
.- Raises
ValueError – When the linearization mode is unknown.
- linearize(input_data=None, force_all=False, force_no_exec=False)¶
Execute the linearized version of the code.
- Parameters
input_data (Optional[Dict[str, Any]]) –
The input data needed to linearize the discipline according to the discipline input grammar. If None, use the
default_inputs
.By default it is set to None.
force_all (bool) –
If False,
_differentiated_inputs
anddifferentiated_output
are used to filter the differentiated variables. otherwise, all outputs are differentiated wrt all inputs.By default it is set to False.
force_no_exec (bool) –
If True, the discipline is not re executed, cache is loaded anyway.
By default it is set to False.
- Returns
The Jacobian of the discipline.
- Return type
Dict[str, Dict[str, numpy.ndarray]]
- property n_calls¶
The number of times the discipline was executed.
Note
This property is multiprocessing safe.
- property n_calls_linearize¶
The number of times the discipline was linearized.
Note
This property is multiprocessing safe.
- notify_status_observers()¶
Notify all status observers that the status has changed.
- Return type
None
- remove_status_observer(obs)¶
Remove an observer for the status.
- Parameters
obs (Any) – The observer to remove.
- Return type
None
- reset_statuses_for_run()¶
Set all the statuses to
PENDING
.- Raises
ValueError – When the discipline cannot be run because of its status.
- Return type
None
- serialize(out_file)¶
Serialize the discipline and store it in a file.
- Parameters
out_file (Union[str, pathlib.Path]) – The path to the file to store the discipline.
- Return type
None
- set_cache_policy(cache_type='SimpleCache', cache_tolerance=0.0, cache_hdf_file=None, cache_hdf_node_name=None, is_memory_shared=True)¶
Set the type of cache to use and the tolerance level.
This method defines when the output data have to be cached according to the distance between the corresponding input data and the input data already cached for which output data are also cached.
The cache can be either a
SimpleCache
recording the last execution or a cache storing all executions, e.g.MemoryFullCache
andHDF5Cache
. Caching data can be either in-memory, e.g.SimpleCache
andMemoryFullCache
, or on the disk, e.g.HDF5Cache
.The attribute
CacheFactory.caches
provides the available caches types.- Parameters
cache_type (str) –
The type of cache.
By default it is set to SimpleCache.
cache_tolerance (float) –
The maximum relative norm of the difference between two input arrays to consider that two input arrays are equal.
By default it is set to 0.0.
cache_hdf_file (Optional[Union[str, pathlib.Path]]) –
The path to the HDF file to store the data; this argument is mandatory when the
HDF5Cache
policy is used.By default it is set to None.
cache_hdf_node_name (Optional[str]) –
The name of the HDF file node to store the discipline data. If None,
name
is used.By default it is set to None.
is_memory_shared (bool) –
Whether to store the data with a shared memory dictionary, which makes the cache compatible with multiprocessing.
By default it is set to True.
- Return type
None
- set_disciplines_statuses(status)¶
Set the sub-disciplines statuses.
To be implemented in subclasses.
- Parameters
status (str) – The status.
- Return type
None
- set_jacobian_approximation(jac_approx_type='finite_differences', jax_approx_step=1e-07, jac_approx_n_processes=1, jac_approx_use_threading=False, jac_approx_wait_time=0)¶
Set the Jacobian approximation method.
Sets the linearization mode to approx_method, sets the parameters of the approximation for further use when calling
linearize()
.- Parameters
jac_approx_type (str) –
The approximation method, either “complex_step” or “finite_differences”.
By default it is set to finite_differences.
jax_approx_step (float) –
The differentiation step.
By default it is set to 1e-07.
jac_approx_n_processes (int) –
The maximum number of processors on which to run.
By default it is set to 1.
jac_approx_use_threading (bool) –
Whether to use threads instead of processes to parallelize the execution; multiprocessing will copy (serialize) all the disciplines, while threading will share all the memory This is important to note if you want to execute the same discipline multiple times, you shall use multiprocessing.
By default it is set to False.
jac_approx_wait_time (float) –
The time waited between two forks of the process / thread.
By default it is set to 0.
- Return type
None
- set_optimal_fd_step(outputs=None, inputs=None, force_all=False, print_errors=False, numerical_error=2.220446049250313e-16)¶
Compute the optimal finite-difference step.
Compute the optimal step for a forward first order finite differences gradient approximation. Requires a first evaluation of the perturbed functions values. The optimal step is reached when the truncation error (cut in the Taylor development), and the numerical cancellation errors (roundoff when doing f(x+step)-f(x))
are approximately equal.
Warning
This calls the discipline execution twice per input variables.
See also
https://en.wikipedia.org/wiki/Numerical_differentiation and “Numerical Algorithms and Digital Representation”, Knut Morken , Chapter 11, “Numerical Differenciation”
- Parameters
inputs –
The inputs wrt which the outputs are linearized. If None, use the
_differentiated_inputs
.By default it is set to None.
outputs –
The outputs to be linearized. If None, use the
_differentiated_outputs
.By default it is set to None.
force_all –
Whether to consider all the inputs and outputs of the discipline;
By default it is set to False.
print_errors –
Whether to display the estimated errors.
By default it is set to False.
numerical_error –
The numerical error associated to the calculation of f. By default, this is the machine epsilon (appx 1e-16), but can be higher when the calculation of f requires a numerical resolution.
By default it is set to 2.220446049250313e-16.
- Returns
The estimated errors of truncation and cancellation error.
- Raises
ValueError – When the Jacobian approximation method has not been set.
- property status¶
The status of the discipline.
- store_local_data(**kwargs)¶
Store discipline data in local data.
- Parameters
kwargs – The data to be stored in
local_data
.**kwargs (Any) –
- Return type
None
- time_stamps = None¶
- class gemseo.mlearning.core.calibration.MLAlgoCalibration(algo, dataset, parameters, calibration_space, measure, measure_options=None, transformer=None, **algo_options)[source]¶
Bases:
object
Calibration of a machine learning algorithm.
- algo_assessor¶
The assessor for the machine learning algorithm.
- Type
- calibration_space¶
The space defining the calibration variables.
- Type
- maximize_objective¶
Whether to maximize the quality measure.
- Type
bool
- optimal_parameters¶
The optimal parameters for the machine learning algorithm.
- Type
Dict[str,ndarray]
- optimal_criterion¶
The optimal quality measure.
- Type
float
- Parameters
algo (str) – The name of a machine learning algorithm.
dataset (Dataset) – A learning dataset.
parameters (Iterable[str]) – The parameters of the machine learning algorithm to calibrate.
calibration_space (DesignSpace) – The space defining the calibration variables.
measure (MLQualityMeasure) – A measure to assess the machine learning algorithm.
measure_options (Optional[MeasureOptionsType]) –
The options of the quality measure. If None, do not use the quality measure options.
By default it is set to None.
transformer (Optional[TransformerType]) –
The transformation strategy for the data groups. If None, do not transform data.
By default it is set to None.
**algo_options (MLAlgoParameterType) – The options of the machine learning algorithm.
- Return type
None
Attributes:
The trained machine learning algorithms.
Methods:
execute
(input_data)Calibrate the machine learning algorithm from a driver.
get_history
(name)Return the history of a variable.
- property algos¶
The trained machine learning algorithms.