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.
Functions:
|
Create a classification model from a learning dataset. |
|
Create a clustering model from a learning dataset. |
|
Create a machine learning algorithm from a learning dataset. |
|
Create a regression model from a learning dataset. |
Get available classification models. |
|
|
Find the available options for a classification model. |
Get available clustering models. |
|
|
Find the available options for clustering model. |
Get available machine learning algorithms. |
|
|
Find the available options for a machine learning algorithm. |
Get available regression models. |
|
|
Find the available options for a regression model. |
|
Import a classification model from a directory. |
|
Import a clustering model from a directory. |
|
Import a machine learning algorithm from a directory. |
|
Import a regression model from a directory. |
- gemseo.mlearning.api.create_classification_model(name, data, transformer={'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 (gemseo.core.dataset.Dataset) – The learning dataset.
transformer (Optional[Dict[str, gemseo.mlearning.transform.transformer.Transformer]]) – The strategies to transform the variables. Values are instances of
Transformer
while keys are names of either variables or groups of variables. If None, do not transform the variables.parameters – The parameters of the classification model.
- Returns
A classification model.
- Return type
gemseo.mlearning.classification.classification.MLClassificationAlgo
- gemseo.mlearning.api.create_clustering_model(name, data, transformer=None, **parameters)[source]¶
Create a clustering model from a learning dataset.
- Parameters
name (str) – The name of the clustering algorithm.
data (gemseo.core.dataset.Dataset) – The learning dataset.
transformer (Optional[Dict[str, gemseo.mlearning.transform.transformer.Transformer]]) – The strategies to transform the variables. Values are instances of
Transformer
while keys are names of either variables or groups of variables. If None, do not transform the variables.parameters – The parameters of the clustering model.
- Returns
A clustering model.
- Return type
- gemseo.mlearning.api.create_mlearning_model(name, data, transformer=None, **parameters)[source]¶
Create a machine learning algorithm from a learning dataset.
- Parameters
name (str) – The name of the machine learning algorithm.
data (gemseo.core.dataset.Dataset) – The learning dataset.
transformer (Optional[Dict[str, gemseo.mlearning.transform.transformer.Transformer]]) – The strategies to transform the variables. Values are instances of
Transformer
while keys are names of either variables or groups of variables. If None, do not transform the variables.parameters – The parameters of the machine learning algorithm.
- Returns
A machine learning model.
- Return type
- gemseo.mlearning.api.create_regression_model(name, data, transformer={'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 (gemseo.core.dataset.Dataset) – The learning dataset.
transformer (Optional[Dict[str, gemseo.mlearning.transform.transformer.Transformer]]) – The strategies to transform the variables. Values are instances of
Transformer
while keys are names of either variables or groups of variables. If None, do not transform the variables.parameters – The parameters of the regression model.
- Returns
A regression model.
- Return type
- 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) – Apply JSON format for the schema.
pretty_print (bool) – Print the schema in a pretty table.
- Returns
The options schema of the classification model.
- Return type
Union[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) – Apply JSON format for the schema.
pretty_print (bool) – Print the schema in a pretty table.
- Returns
The options schema of the clustering model.
- Return type
Union[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) – Apply JSON format for the schema.
pretty_print (bool) – Whether to print the schema in a pretty table.
- Returns
The options schema of the machine learning algorithm.
- Return type
Union[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) – Apply JSON format for the schema.
pretty_print (bool) – Print the schema in a pretty table.
- Returns
The options schema of the regression model.
- Return type
Union[Dict[str, str], str]
- gemseo.mlearning.api.import_classification_model(directory)[source]¶
Import a classification model from a directory.
- Parameters
directory (str) – The name of the directory.
- Returns
A classification model.
- Return type
gemseo.mlearning.classification.classification.MLClassificationAlgo
- gemseo.mlearning.api.import_clustering_model(directory)[source]¶
Import a clustering model from a directory.
- Parameters
directory (str) – The name of the directory.
- Returns
A clustering model.
- Return type
- gemseo.mlearning.api.import_mlearning_model(directory)[source]¶
Import a machine learning algorithm from a directory.
- Parameters
directory (str) – The name of the directory.
- Returns
A machine learning model.
- Return type