gemseo / problems / dataset

rosenbrock module

Rosenbrock dataset

This Dataset contains 100 evaluations of the well-known Rosenbrock function:

\[f(x,y)=(1-x)^2+100(y-x^2)^2\]

This function is known for its global minimum at point (1,1), its banana valley and the difficulty to reach its minimum.

This Dataset is based on a full-factorial design of experiments.

More information about the Rosenbrock function

Classes:

RosenbrockDataset([name, by_group, ...])

Rosenbrock dataset parametrization.

class gemseo.problems.dataset.rosenbrock.RosenbrockDataset(name='Rosenbrock', by_group=True, n_samples=100, categorize=True, opt_naming=True)[source]

Bases: gemseo.core.dataset.Dataset

Rosenbrock dataset parametrization.

Constructor.

Parameters
  • name (str) –

    name of the dataset.

    By default it is set to Rosenbrock.

  • by_group (bool) –

    if True, store the data by group. Otherwise, store them by variables. Default: True

    By default it is set to True.

  • n_samples (int) –

    number of samples

    By default it is set to 100.

  • categorize (bool) –

    distinguish between the different groups of variables. Default: True.

    By default it is set to True.

Parma bool opt_naming

use an optimization naming. Default: True.

Attributes:

DEFAULT_GROUP

DEFAULT_NAMES

DESIGN_GROUP

FUNCTION_GROUP

GRADIENT_GROUP

HDF5_CACHE

INPUT_GROUP

MEMORY_FULL_CACHE

OUTPUT_GROUP

PARAMETER_GROUP

columns_names

The names of the columns of the dataset.

groups

The sorted names of the groups of variables.

n_samples

The number of samples.

n_variables

The number of variables.

row_names

The names of the rows.

variables

The sorted names of the variables.

Methods:

add_group(group, data[, variables, sizes, ...])

Add data related to a group.

add_variable(name, data[, group, cache_as_input])

Add data related to a variable.

compare(value_1, logical_operator, value_2)

Compare either a variable and a value or a variable and another variable.

export_to_cache([inputs, outputs, ...])

Export the dataset to a cache.

export_to_dataframe([copy])

Export the dataset to a pandas Dataframe.

find(comparison)

Find the entries for which a comparison is satisfied.

get_all_data([by_group, as_dict])

Get all the data stored in the dataset.

get_available_plots()

Return the available plot methods.

get_data_by_group(group[, as_dict])

Get the data for a specific group name.

get_data_by_names(names[, as_dict])

Get the data for specific names of variables.

get_group(variable_name)

Get the name of the group that contains a variable.

get_names(group_name)

Get the names of the variables of a group.

get_normalized_dataset([excluded_variables, ...])

Get a normalized copy of the dataset.

is_empty()

Check if the dataset is empty.

is_group(name)

Check if a name is a group name.

is_nan()

Check if an entry contains NaN.

is_variable(name)

Check if a name is a variable name.

n_variables_by_group(group)

The number of variables for a group.

plot(name[, show, save])

Plot the dataset from a DatasetPlot.

remove(entries)

Remove entries.

set_from_array(data[, variables, sizes, ...])

Set the dataset from an array.

set_from_file(filename[, variables, sizes, ...])

Set the dataset from a file.

set_metadata(name, value)

Set a metadata attribute.

DEFAULT_GROUP = 'parameters'
DEFAULT_NAMES = {'design_parameters': 'dp', 'functions': 'func', 'inputs': 'in', 'outputs': 'out', 'parameters': 'x'}
DESIGN_GROUP = 'design_parameters'
FUNCTION_GROUP = 'functions'
GRADIENT_GROUP = 'gradients'
HDF5_CACHE = 'HDF5Cache'
INPUT_GROUP = 'inputs'
MEMORY_FULL_CACHE = 'MemoryFullCache'
OUTPUT_GROUP = 'outputs'
PARAMETER_GROUP = 'parameters'
add_group(group, data, variables=None, sizes=None, pattern=None, cache_as_input=True)

Add data related to a group.

Parameters
  • group (str) – The name of the group of data to be added.

  • data (numpy.ndarray) – The data to be added.

  • variables (Optional[List[str]]) –

    The names of the variables. If None, use default names based on a pattern.

    By default it is set to None.

  • sizes (Optional[Dict[str, int]]) –

    The sizes of the variables. If None, assume that all the variables have a size equal to 1.

    By default it is set to None.

  • pattern (Optional[str]) –

    The name of the variable to be used as a pattern when variables is None. If None, use the Dataset.DEFAULT_NAMES for this group if it exists. Otherwise, use the group name.

    By default it is set to None.

  • cache_as_input (bool) –

    If True, cache these data as inputs when the cache is exported to a cache.

    By default it is set to True.

Return type

str

add_variable(name, data, group='parameters', cache_as_input=True)

Add data related to a variable.

Parameters
  • name (str) – The name of the variable to be stored.

  • data (numpy.ndarray) – The data to be stored.

  • group (str) –

    The name of the group related to this variable.

    By default it is set to parameters.

  • cache_as_input (bool) –

    If True, cache these data as inputs when the cache is exported to a cache.

    By default it is set to True.

Return type

None

property columns_names

The names of the columns of the dataset.

compare(value_1, logical_operator, value_2, component_1=0, component_2=0)

Compare either a variable and a value or a variable and another variable.

Parameters
  • value_1 (Union[str, float]) – The first value, either a variable name or a numeric value.

  • logical_operator (str) – The logical operator, either “==”, “<”, “<=”, “>” or “>=”.

  • value_2 (Union[str, float]) – The second value, either a variable name or a numeric value.

  • component_1 (int) –

    If value_1 is a variable name, component_1 corresponds to its component used in the comparison.

    By default it is set to 0.

  • component_2 (int) –

    If value_2 is a variable name, component_2 corresponds to its component used in the comparison.

    By default it is set to 0.

Returns

Whether the comparison is valid for the different entries.

Return type

numpy.ndarray

export_to_cache(inputs=None, outputs=None, cache_type='MemoryFullCache', cache_hdf_file=None, cache_hdf_node_name=None, **options)

Export the dataset to a cache.

Parameters
  • inputs (Optional[Iterable[str]]) –

    The names of the inputs to cache. If None, use all inputs.

    By default it is set to None.

  • outputs (Optional[Iterable[str]]) –

    The names of the outputs to cache. If None, use all outputs.

    By default it is set to None.

  • cache_type (str) –

    The type of cache to use.

    By default it is set to MemoryFullCache.

  • cache_hdf_file (Optional[str]) –

    The name of the HDF file to store the data. Required if the type of the cache is ‘HDF5Cache’.

    By default it is set to None.

  • cache_hdf_node_name (Optional[str]) –

    The name of the HDF node to store the discipline. If None, use the name of the dataset.

    By default it is set to None.

Returns

A cache containing the dataset.

Return type

gemseo.core.cache.AbstractFullCache

export_to_dataframe(copy=True)

Export the dataset to a pandas Dataframe.

Parameters

copy (bool) –

If True, copy data. Otherwise, use reference.

By default it is set to True.

Returns

A pandas DataFrame containing the dataset.

Return type

DataFrame

static find(comparison)

Find the entries for which a comparison is satisfied.

This search uses a boolean 1D array whose length is equal to the length of the dataset.

Parameters

comparison (numpy.ndarray) – A boolean vector whose length is equal to the number of samples.

Returns

The indices of the entries for which the comparison is satisfied.

Return type

List[int]

get_all_data(by_group=True, as_dict=False)

Get all the data stored in the dataset.

The data can be returned either as a dictionary indexed by the names of the variables, or as an array concatenating them, accompanied with the names and sizes of the variables.

The data can also classified by groups of variables.

Parameters
  • by_group

    If True, sort the data by group.

    By default it is set to True.

  • as_dict

    If True, return the data as a dictionary.

    By default it is set to False.

Returns

All the data stored in the dataset.

Return type

Union[Dict[str, Union[Dict[str, numpy.ndarray], numpy.ndarray]], Tuple[Union[numpy.ndarray, Dict[str, numpy.ndarray]], List[str], Dict[str, int]]]

get_available_plots()

Return the available plot methods.

Return type

List[str]

get_data_by_group(group, as_dict=False)

Get the data for a specific group name.

Parameters
  • group (str) – The name of the group.

  • as_dict (bool) –

    If True, return values as dictionary.

    By default it is set to False.

Returns

The data related to the group.

Return type

Union[numpy.ndarray, Dict[str, numpy.ndarray]]

get_data_by_names(names, as_dict=True)

Get the data for specific names of variables.

Parameters
  • names (Union[str, Iterable[str]]) – The names of the variables.

  • as_dict (bool) –

    If True, return values as dictionary.

    By default it is set to True.

Returns

The data related to the variables.

Return type

Union[numpy.ndarray, Dict[str, numpy.ndarray]]

get_group(variable_name)

Get the name of the group that contains a variable.

Parameters

variable_name (str) – The name of the variable.

Returns

The group to which the variable belongs.

Return type

str

get_names(group_name)

Get the names of the variables of a group.

Parameters

group_name (str) – The name of the group.

Returns

The names of the variables of the group.

Return type

List[str]

get_normalized_dataset(excluded_variables=None, excluded_groups=None)

Get a normalized copy of the dataset.

Parameters
  • excluded_variables (Optional[Sequence[str]]) –

    The names of the variables not to be normalized. If None, normalize all the variables.

    By default it is set to None.

  • excluded_groups (Optional[Sequence[str]]) –

    The names of the groups not to be normalized. If None, normalize all the groups.

    By default it is set to None.

Returns

A normalized dataset.

Return type

gemseo.core.dataset.Dataset

property groups

The sorted names of the groups of variables.

is_empty()

Check if the dataset is empty.

Returns

Whether the dataset is empty.

Return type

bool

is_group(name)

Check if a name is a group name.

Parameters

name (str) – A name of a group.

Returns

Whether the name is a group name.

Return type

bool

is_nan()

Check if an entry contains NaN.

Returns

Whether any entries is NaN or not.

Return type

numpy.ndarray

is_variable(name)

Check if a name is a variable name.

Parameters

name (str) – A name of a variable.

Returns

Whether the name is a variable name.

Return type

bool

property n_samples

The number of samples.

property n_variables

The number of variables.

n_variables_by_group(group)

The number of variables for a group.

Parameters

group (str) – The name of a group.

Returns

The group dimension.

Return type

int

plot(name, show=True, save=False, **options)

Plot the dataset from a DatasetPlot.

See Dataset.get_available_plots()

Parameters
  • name (str) – The name of the post-processing, which is the name of a class inheriting from DatasetPlot.

  • show (bool) –

    If True, display the figure.

    By default it is set to True.

  • save (bool) –

    If True, save the figure.

    By default it is set to False.

  • options – The options for the post-processing.

Return type

None

remove(entries)

Remove entries.

Parameters

entries (Union[List[int], numpy.ndarray]) – The entries to be removed, either indices or a boolean 1D array whose length is equal to the length of the dataset and elements to delete are coded True.

Return type

None

property row_names

The names of the rows.

set_from_array(data, variables=None, sizes=None, groups=None, default_name=None)

Set the dataset from an array.

Parameters
  • data (numpy.ndarray) – The data to be stored.

  • variables (Optional[List[str]]) –

    The names of the variables. If None, use one default name per column of the array based on the pattern ‘default_name’.

    By default it is set to None.

  • sizes (Optional[Dict[str, int]]) –

    The sizes of the variables. If None, assume that all the variables have a size equal to 1.

    By default it is set to None.

  • groups (Optional[Dict[str, str]]) –

    The groups of the variables. If None, use Dataset.DEFAULT_GROUP for all the variables.

    By default it is set to None.

  • default_name (Optional[str]) –

    The name of the variable to be used as a pattern when variables is None. If None, use the Dataset.DEFAULT_NAMES for this group if it exists. Otherwise, use the group name.

    By default it is set to None.

Return type

None

set_from_file(filename, variables=None, sizes=None, groups=None, delimiter=',', header=True)

Set the dataset from a file.

Parameters
  • filename (str) – The name of the file containing the data.

  • variables (Optional[List[str]]) –

    The names of the variables. If None and header is True, read the names from the first line of the file. If None and header is False, use default names based on the patterns the Dataset.DEFAULT_NAMES associated with the different groups.

    By default it is set to None.

  • sizes (Optional[Dict[str, int]]) –

    The sizes of the variables. If None, assume that all the variables have a size equal to 1.

    By default it is set to None.

  • groups (Optional[Dict[str, str]]) –

    The groups of the variables. If None, use Dataset.DEFAULT_GROUP for all the variables.

    By default it is set to None.

  • delimiter (str) –

    The field delimiter.

    By default it is set to ,.

  • header (bool) –

    If True, read the names of the variables on the first line of the file.

    By default it is set to True.

Return type

None

set_metadata(name, value)

Set a metadata attribute.

Parameters
  • name (str) – The name of the metadata attribute.

  • value (Any) – The value of the metadata attribute.

Return type

None

property variables

The sorted names of the variables.