Unsupervised learning

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)

Unsupervised machine learning algorithm.

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

learning_set

The learning dataset.

Type

Dataset

parameters

The parameters of the machine learning algorithm.

Type

Dict[str,MLAlgoParameterType]

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.

Type

Dict[str,Transformer]

algo

The interfaced machine learning algorithm.

Type

Any

input_names

The names of the variables.

Type

List[str]

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

Parameters

• data (Dataset) – The learning dataset.

• transformer (Optional[TransformerType]) –

  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.

  By default it is set to None.

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

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

  By default it is set to None.

• **parameters (MLAlgoParameterType) – The parameters of the machine learning algorithm.

Return type

None

Classes:

DataFormatters()

Decorators for the internal MLAlgo methods.

Attributes:

is_trained	Return whether the algorithm is trained.
learning_samples_indices	The indices of the learning samples used for the training.

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.

class DataFormatters

Decorators for the internal MLAlgo methods.

property is_trained

Return whether the algorithm is trained.

learn(samples=None)

Train the machine learning algorithm from the learning dataset.

Parameters

samples (Optional[Sequence[int]]) –

The indices of the learning samples. If None, use the whole learning dataset.

By default it is set to None.

Return type

None

property learning_samples_indices

The indices of the learning samples used for the training.

load_algo(directory)

Load a machine learning algorithm from a directory.

Parameters

directory (Union[str, pathlib.Path]) – 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.

  By default it is set to None.

• path (Union[str, pathlib.Path]) –

  The path to parent directory where to create the directory.

  By default it is set to ..

• save_learning_set (bool) –

  Whether to save the learning set or get rid of it to lighten the saved files.

  By default it is set to False.

Returns

The path to the directory where the algorithm is saved.

Return type

str