gemseo / datasets

io_dataset module

A Dataset to store input and output values.

class gemseo.datasets.io_dataset.IODataset(data=None, index=None, columns=None, dtype=None, copy=None, *, dataset_name='')[source]

Bases: Dataset

A Dataset to store input and output values.

Warning

A Dataset behaves like any multi-index DataFrame but its instantiation using the constructor dataset = Dataset(data, ...) can lead to some inconsistencies (multi-index levels, index values, dtypes, …). Hence, the construction from the dedicated methods is recommended, e.g. dataset = Dataset(); dataset.add_variable("x", data).

Notes

The columns of a data structure (NumPy array, DataFrame, Dataset, …) are called features. The features of a Dataset include all the components of all the variables of all the groups.

Initialize self. See help(type(self)) for accurate signature.

Parameters:
  • dataset_name (str) –

    The name of the dataset.

    By default it is set to “”.

  • data (ndarray | Iterable | dict | DataFrame | None) –

  • index (Axes | None) –

  • columns (Axes | None) –

  • dtype (Dtype | None) –

  • copy (bool | None) –

add_input_group(data, variable_names=(), variable_names_to_n_components=None)[source]

Add the data related to the input group.

Parameters:
  • data (DataType) – The data.

  • variable_names (StrColumnType) –

    The names of the variables. If empty, use DEFAULT_VARIABLE_NAME.

    By default it is set to ().

  • variable_names_to_n_components (dict[str, int] | None) – The number of components of the variables. If variable_names is empty, this argument is not considered. If None, assume that all the variables have a single component.

Return type:

None

add_input_variable(variable_name, data, components=())[source]

Add data related to an input variable.

Parameters:
  • variable_name (str) – The name of the variable.

  • data (ndarray | Iterable[Any] | Any) – The data, either an array shaped as (n_entries, n_features), an array shaped as (n_entries,) that will be reshaped as (n_entries, 1) or a scalar that will be converted into an array shaped as (n_entries, 1).

  • components (int | Iterable[int]) –

    The components considered. If empty, use [0, ..., n_features].

    By default it is set to ().

Return type:

None

add_output_group(data, variable_names=(), variable_names_to_n_components=None)[source]

Add the data related to the output group.

Parameters:
  • data (DataType) – The data.

  • variable_names (StrColumnType) –

    The names of the variables. If empty, use DEFAULT_VARIABLE_NAME.

    By default it is set to ().

  • variable_names_to_n_components (dict[str, int] | None) – The number of components of the variables. If variable_names is empty, this argument is not considered. If None, assume that all the variables have a single component.

Return type:

None

add_output_variable(variable_name, data, components=())[source]

Add data related to an output variable.

Parameters:
  • variable_name (str) – The name of the variable.

  • data (ndarray | Iterable[Any] | Any) – The data, either an array shaped as (n_entries, n_features), an array shaped as (n_entries,) that will be reshaped as (n_entries, 1) or a scalar that will be converted into an array shaped as (n_entries, 1).

  • components (int | Iterable[int]) –

    The components considered. If empty, use [0, ..., n_features].

    By default it is set to ().

Return type:

None

INPUT_GROUP: Final[str] = 'inputs'

The group name for the input variables.

OUTPUT_GROUP: Final[str] = 'outputs'

The group name for the output variables.

property input_dataset: IODataset

The view of the input dataset.

property input_names: list[str]

The names of the inputs.

property n_samples: int

The number of samples.

property output_dataset: IODataset

The view of the output dataset.

property output_names: list[str]

The names of the outputs.

property samples: list[int | str]

The ordered samples.

Examples using IODataset

Empirical estimation of statistics

Empirical estimation of statistics

Parametric estimation of statistics

Parametric estimation of statistics

Store observables

Store observables

Diagonal design of experiments

Diagonal design of experiments

Scalable diagonal discipline

Scalable diagonal discipline

Use a design of experiments from a file

Use a design of experiments from a file

Use a design of experiments from an array

Use a design of experiments from an array

Create a surrogate discipline

Create a surrogate discipline

Plug a surrogate discipline in a Scenario

Plug a surrogate discipline in a Scenario

Calibration of a polynomial regression

Calibration of a polynomial regression

Machine learning algorithm selection example

Machine learning algorithm selection example

Classification API

Classification API

K nearest neighbors classification

K nearest neighbors classification

Random forest classification

Random forest classification

API

API

Gaussian Mixtures

Gaussian Mixtures

K-means

K-means

KL-SVD on Burgers equation

KL-SVD on Burgers equation

Mixture of experts with PCA on Burgers dataset

Mixture of experts with PCA on Burgers dataset

PCA on Burgers equation

PCA on Burgers equation

MSE example - test-train split

MSE example - test-train split

Quality measure for surrogate model comparison

Quality measure for surrogate model comparison

API

API

Advanced mixture of experts

Advanced mixture of experts

GP regression

GP regression

Linear regression

Linear regression

Mixture of experts

Mixture of experts

PCE regression

PCE regression

Polynomial regression

Polynomial regression

RBF regression

RBF regression

Random forest regression

Random forest regression

Save and Load

Save and Load

The input-output dataset

The input-output dataset

Burgers dataset

Burgers dataset

Iris dataset

Iris dataset

Rosenbrock dataset

Rosenbrock dataset

Andrews curves

Andrews curves

Boxplot

Boxplot

Parallel coordinates

Parallel coordinates

Scatter matrix

Scatter matrix

Parameter space

Parameter space