gemseo.mlearning package#

Machine learning functionalities.

This module proposes many high-level functions for creating and loading machine learning models.

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

Create a classification model from a training dataset.

Parameters:

  • name (str) -- The name of the classification algorithm.

  • data (IODataset) -- The training dataset.

  • transformer (TransformerType) --

    The strategies to transform the variables. Values are instances of BaseTransformer 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.transformers.scaler.min_max_scaler.MinMaxScaler object at 0x7c2f6f74d5b0>}.

  • parameters -- The parameters of the classification model.

Returns:

A classification model.

Return type:

BaseClassifier

create_clustering_model(name, data, transformer=mappingproxy({}), **parameters)[source]#

Create a clustering model from a training dataset.

Parameters:

  • name (str) -- The name of the clustering algorithm.

  • data (Dataset) -- The training dataset.

  • transformer (TransformerType) --

    The strategies to transform the variables. Values are instances of BaseTransformer 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:

BaseClusterer

create_mlearning_model(name, data, transformer=mappingproxy({}), **parameters)[source]#

Create a machine learning algorithm from a training dataset.

Parameters:

  • name (str) -- The name of the machine learning algorithm.

  • data (Dataset) -- The training dataset.

  • transformer (TransformerType) --

    The strategies to transform the variables. Values are instances of BaseTransformer 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:

BaseMLAlgo

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

Create a regression model from a training dataset.

Parameters:

  • name (str) -- The name of the regression algorithm.

  • data (IODataset) -- The training dataset.

  • transformer (TransformerType) --

    The strategies to transform the variables. Values are instances of BaseTransformer 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.transformers.scaler.min_max_scaler.MinMaxScaler object at 0x7c2f6f74c140>, 'outputs': <gemseo.mlearning.transformers.scaler.min_max_scaler.MinMaxScaler object at 0x7c2f6f74eb40>}.

  • parameters -- The parameters of the regression model.

Returns:

A regression model.

Return type:

BaseRegressor

get_classification_models()[source]#

Get available classification models.

Returns:

The available classification models.

Return type:

list[str]

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

get_clustering_models()[source]#

Get available clustering models.

Returns:

The available clustering models.

Return type:

list[str]

get_clustering_options(model_name, output_json=False, pretty_print=True)[source]#

Find the available options for a 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

get_mlearning_models()[source]#

Get available machine learning algorithms.

Returns:

The available machine learning algorithms.

Return type:

list[str]

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

get_regression_models()[source]#

Get available regression models.

Returns:

The available regression models.

Return type:

list[str]

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

Subpackages#