gemseo / mlearning

api module

Machine learning API.

The machine learning API provides methods for creating new and loading existing machine learning models. It also provides methods for listing available models and options.

gemseo.mlearning.api.create_classification_model(name, data, transformer=mappingproxy({'inputs': <gemseo.mlearning.transform.scaler.min_max_scaler.MinMaxScaler object>}), **parameters)[source]

Create a classification model from a learning dataset.

Parameters:
  • name (str) – The name of the classification algorithm.

  • data (Dataset) – The learning dataset.

  • transformer (MutableMapping[str, Union[str, Tuple[str, Mapping[str, Any]], Transformer]]) –

    The strategies to transform the variables. Values are instances of Transformer while keys are names of either variables or groups of variables. If IDENTITY, do not transform the variables.

    By default it is set to {‘inputs’: <gemseo.mlearning.transform.scaler.min_max_scaler.MinMaxScaler object at 0x7f2c2ef44f10>}.

  • parameters – The parameters of the classification model.

Returns:

A classification model.

Return type:

MLClassificationAlgo

gemseo.mlearning.api.create_clustering_model(name, data, transformer=mappingproxy({}), **parameters)[source]

Create a clustering model from a learning dataset.

Parameters:
  • name (str) – The name of the clustering algorithm.

  • data (Dataset) – The learning dataset.

  • transformer (MutableMapping[str, Union[str, Tuple[str, Mapping[str, Any]], Transformer]]) –

    The strategies to transform the variables. Values are instances of Transformer while keys are names of either variables or groups of variables. If IDENTITY, do not transform the variables.

    By default it is set to {}.

  • parameters – The parameters of the clustering model.

Returns:

A clustering model.

Return type:

MLClusteringAlgo

gemseo.mlearning.api.create_mlearning_model(name, data, transformer=mappingproxy({}), **parameters)[source]

Create a machine learning algorithm from a learning dataset.

Parameters:
  • name (str) – The name of the machine learning algorithm.

  • data (Dataset) – The learning dataset.

  • transformer (MutableMapping[str, Union[str, Tuple[str, Mapping[str, Any]], Transformer]]) –

    The strategies to transform the variables. Values are instances of Transformer while keys are names of either variables or groups of variables. If IDENTITY, do not transform the variables.

    By default it is set to {}.

  • parameters – The parameters of the machine learning algorithm.

Returns:

A machine learning model.

Return type:

MLAlgo

gemseo.mlearning.api.create_regression_model(name, data, transformer=mappingproxy({'inputs': <gemseo.mlearning.transform.scaler.min_max_scaler.MinMaxScaler object>, 'outputs': <gemseo.mlearning.transform.scaler.min_max_scaler.MinMaxScaler object>}), **parameters)[source]

Create a regression model from a learning dataset.

Parameters:
  • name (str) – The name of the regression algorithm.

  • data (Dataset) – The learning dataset.

  • transformer (MutableMapping[str, Union[str, Tuple[str, Mapping[str, Any]], Transformer]]) –

    The strategies to transform the variables. Values are instances of Transformer while keys are names of either variables or groups of variables. If IDENTITY, do not transform the variables.

    By default it is set to {‘inputs’: <gemseo.mlearning.transform.scaler.min_max_scaler.MinMaxScaler object at 0x7f2c2ef2e5b0>, ‘outputs’: <gemseo.mlearning.transform.scaler.min_max_scaler.MinMaxScaler object at 0x7f2c2ef2ea60>}.

  • parameters – The parameters of the regression model.

Returns:

A regression model.

Return type:

MLRegressionAlgo

gemseo.mlearning.api.get_classification_models()[source]

Get available classification models.

Returns:

The available classification models.

Return type:

list[str]

gemseo.mlearning.api.get_classification_options(model_name, output_json=False, pretty_print=True)[source]

Find the available options for a classification model.

Parameters:
  • model_name (str) – The name of the classification model.

  • output_json (bool) –

    Whether to apply JSON format for the schema.

    By default it is set to False.

  • pretty_print (bool) –

    Print the schema in a pretty table.

    By default it is set to True.

Returns:

The options schema of the classification model.

Return type:

dict[str, str] | str

gemseo.mlearning.api.get_clustering_models()[source]

Get available clustering models.

Returns:

The available clustering models.

Return type:

list[str]

gemseo.mlearning.api.get_clustering_options(model_name, output_json=False, pretty_print=True)[source]

Find the available options for clustering model.

Parameters:
  • model_name (str) – The name of the clustering model.

  • output_json (bool) –

    Whether to apply JSON format for the schema.

    By default it is set to False.

  • pretty_print (bool) –

    Print the schema in a pretty table.

    By default it is set to True.

Returns:

The options schema of the clustering model.

Return type:

dict[str, str] | str

gemseo.mlearning.api.get_mlearning_models()[source]

Get available machine learning algorithms.

Returns:

The available machine learning algorithms.

Return type:

list[str]

gemseo.mlearning.api.get_mlearning_options(model_name, output_json=False, pretty_print=True)[source]

Find the available options for a machine learning algorithm.

Parameters:
  • model_name (str) – The name of the machine learning algorithm.

  • output_json (bool) –

    Whether to apply JSON format for the schema.

    By default it is set to False.

  • pretty_print (bool) –

    Whether to print the schema in a pretty table.

    By default it is set to True.

Returns:

The options schema of the machine learning algorithm.

Return type:

dict[str, str] | str

gemseo.mlearning.api.get_regression_models()[source]

Get available regression models.

Returns:

The available regression models.

Return type:

list[str]

gemseo.mlearning.api.get_regression_options(model_name, output_json=False, pretty_print=True)[source]

Find the available options for a regression model.

Parameters:
  • model_name (str) – The name of the regression model.

  • output_json (bool) –

    Whether to apply JSON format for the schema.

    By default it is set to False.

  • pretty_print (bool) –

    Print the schema in a pretty table.

    By default it is set to True.

Returns:

The options schema of the regression model.

Return type:

dict[str, str] | str

gemseo.mlearning.api.import_classification_model(directory)[source]

Import a classification model from a directory.

Parameters:

directory (str | Path) – The path to the directory.

Returns:

A classification model.

Return type:

MLClassificationAlgo

gemseo.mlearning.api.import_clustering_model(directory)[source]

Import a clustering model from a directory.

Parameters:

directory (str | Path) – The path to the directory.

Returns:

A clustering model.

Return type:

MLClusteringAlgo

gemseo.mlearning.api.import_mlearning_model(directory)[source]

Import a machine learning algorithm from a directory.

Parameters:

directory (str | Path) – The path to the directory.

Returns:

A machine learning model.

Return type:

MLAlgo

gemseo.mlearning.api.import_regression_model(directory)[source]

Import a regression model from a directory.

Parameters:

directory (str | Path) – The path of the directory.

Returns:

A regression model.

Return type:

MLRegressionAlgo

Examples using create_classification_model

Classification API

Classification API

Classification API
K nearest neighbors classification

K nearest neighbors classification

K nearest neighbors classification
Random forest classification

Random forest classification

Random forest classification

Examples using create_clustering_model

API

API

API
Gaussian Mixtures

Gaussian Mixtures

Gaussian Mixtures
K-means

K-means

K-means

Examples using create_regression_model

Mixture of experts with PCA on Burgers dataset

Mixture of experts with PCA on Burgers dataset

Mixture of experts with PCA on Burgers dataset
MSE example - test-train split

MSE example - test-train split

MSE example - test-train split
Quality measure for surrogate model comparison

Quality measure for surrogate model comparison

Quality measure for surrogate model comparison
API

API

API
Advanced mixture of experts

Advanced mixture of experts

Advanced mixture of experts
GP regression

GP regression

GP regression
Linear regression

Linear regression

Linear regression
Mixture of experts

Mixture of experts

Mixture of experts
PCE regression

PCE regression

PCE regression
Polynomial regression

Polynomial regression

Polynomial regression
RBF regression

RBF regression

RBF regression
Random forest regression

Random forest regression

Random forest regression
Save and Load

Save and Load

Save and Load

Examples using get_classification_models

Classification API

Classification API

Classification API

Examples using get_classification_options

Classification API

Classification API

Classification API

Examples using get_clustering_models

API

API

API

Examples using get_clustering_options

API

API

API

Examples using get_regression_models

API

API

API

Examples using get_regression_options

API

API

API

Examples using import_regression_model

PCE regression

PCE regression

PCE regression
Save and Load

Save and Load

Save and Load