gemseo / mlearning / core

# unsupervised module¶

This module contains the base class for the unsupervised machine learning algorithms.

The unsupervised module implements the concept of unsupervised machine learning models, where the data has no notion of input or output.

This concept is implemented through the MLUnsupervisedAlgo class, which inherits from the MLAlgo class.

Classes:

 MLUnsupervisedAlgo(data[, transformer, …]) Unsupervised machine learning algorithm.
class gemseo.mlearning.core.unsupervised.MLUnsupervisedAlgo(data, transformer=None, var_names=None, **parameters)[source]

Unsupervised machine learning algorithm.

Inheriting classes shall overload the MLUnsupervisedAlgo._fit() method.

Attributes
• learning_set (Dataset) – The learning dataset.

• parameters (Dict[str,MLAlgoParameterType]) – The parameters of the machine learning algorithm.

• transformer (Dict[str,Transformer]) – The strategies to transform the variables. The values are instances of Transformer while the keys are the names of either the variables or the groups of variables, e.g. “inputs” or “outputs” in the case of the regression algorithms. If a group is specified, the Transformer will be applied to all the variables of this group. If None, do not transform the variables.

• algo (Any) – The interfaced machine learning algorithm.

• input_names (List[str]) – The names of the variables.

Parameters
• data (Dataset) –

• transformer (Optional[TransformerType]) –

• var_names (Optional[Iterable[str]]) –

• parameters (MLAlgoParameterType) –

Return type

None

Parameters: data: The learning dataset. transformer: The strategies to transform the variables.

The values are instances of Transformer while the keys are the names of either the variables or the groups of variables, e.g. “inputs” or “outputs” in the case of the regression algorithms. If a group is specified, the Transformer will be applied to all the variables of this group. If None, do not transform the variables.

**parameters: The parameters of the machine learning algorithm. var_names: The names of the variables.

If None, consider all variables mentioned in the learning dataset.

Attributes:

 ABBR FILENAME LIBRARY is_trained Return whether the algorithm is trained.

Classes:

 Decorators for the internal MLAlgo methods.

Methods:

 learn([samples]) Train the machine learning algorithm from the learning dataset. load_algo(directory) Load a machine learning algorithm from a directory. save([directory, path, save_learning_set]) Save the machine learning algorithm.
ABBR = 'MLUnupervisedAlgo'
class DataFormatters

Bases: object

Decorators for the internal MLAlgo methods.

FILENAME = 'ml_algo.pkl'
LIBRARY = None
property is_trained

Return whether the algorithm is trained.

learn(samples=None)[source]

Train the machine learning algorithm from the learning dataset.

Parameters

samples (Optional[List[int]]) – The indices of the learning samples. If None, use the whole learning dataset.

Return type

None

Load a machine learning algorithm from a directory.

Parameters

directory (str) – The path to the directory where the machine learning algorithm is saved.

Return type

None

save(directory=None, path='.', save_learning_set=False)

Save the machine learning algorithm.

Parameters
• directory (Optional[str]) – The name of the directory to save the algorithm.

• path (str) – The path to parent directory where to create the directory.

• save_learning_set (bool) – If False, do not save the learning set to lighten the saved files.

Returns

The path to the directory where the algorithm is saved.

Return type

str