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:
|
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:
The names of the columns of the dataset.
The sorted names of the groups of variables.
The number of samples.
The number of variables.
The names of the rows.
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.
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
- 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
- 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.