gemseo / problems / dataset

iris module

Iris dataset

This is one of the best known Dataset to be found in the machine learning literature.

It was introduced by the statistician Ronald Fisher in his 1936 paper “The use of multiple measurements in taxonomic problems”, Annals of Eugenics. 7 (2): 179–188.

It contains 150 instances of iris plants:

  • 50 Iris Setosa,

  • 50 Iris Versicolour,

  • 50 Iris Virginica.

Each instance is characterized by:

  • its sepal length in cm,

  • its sepal width in cm,

  • its petal length in cm,

  • its petal width in cm.

This Dataset can be used for either clustering purposes or classification ones.

More information about the Iris dataset

Classes:

IrisDataset([name, by_group, as_io])

Iris dataset parametrization.

class gemseo.problems.dataset.iris.IrisDataset(name='Iris', by_group=True, as_io=False)[source]

Bases: gemseo.core.dataset.Dataset

Iris dataset parametrization.

Constructor.

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.

  • sizes (Optional[Dict[str, int]]) – The sizes of the variables. If None, assume that all the variables have a size equal to 1.

  • 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.

  • cache_as_input (bool) – If True, cache these data as inputs when the cache is exported to a cache.

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.

  • cache_as_input (bool) – If True, cache these data as inputs when the cache is exported to a cache.

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.

  • component_2 (int) – If value_2 is a variable name, component_2 corresponds to its component used in the comparison.

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.

  • outputs (Optional[Iterable[str]]) – The names of the outputs to cache. If None, use all outputs.

  • cache_type (str) – The type of cache to use.

  • cache_hdf_file (Optional[str]) – The name of the HDF file to store the data. Required if the type of the cache is ‘HDF5Cache’.

  • cache_hdf_node_name (Optional[str]) – The name of the HDF node to store the discipline. If None, use the name of the dataset.

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.

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.

  • as_dict – If True, return the data as a dictionary.

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.

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.

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.

  • excluded_groups (Optional[Sequence[str]]) – The names of the groups not to be normalized. If None, normalize all the groups.

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.

  • save (bool) – If True, save the figure.

  • 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’.

  • sizes (Optional[Dict[str, int]]) – The sizes of the variables. If None, assume that all the variables have a size equal to 1.

  • groups (Optional[Dict[str, str]]) – The groups of the variables. If None, use Dataset.DEFAULT_GROUP for all the variables.

  • 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.

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.

  • sizes (Optional[Dict[str, int]]) – The sizes of the variables. If None, assume that all the variables have a size equal to 1.

  • groups (Optional[Dict[str, str]]) – The groups of the variables. If None, use Dataset.DEFAULT_GROUP for all the variables.

  • delimiter (str) – The field delimiter.

  • header (bool) – If True, read the names of the variables on the first line of the file.

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.