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.
- 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.- Parameters
data (Dataset) – The learning dataset.
transformer (Mapping[str, TransformerType] | None) –
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, theTransformer
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 (Iterable[str] | None) –
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.
- Raises
ValueError – When both the variable and the group it belongs to have a transformer.
- Return type
None
- class DataFormatters
Decorators for the internal MLAlgo methods.
- Noindex
- learn(samples=None, fit_transformers=True)
Train the machine learning algorithm from the learning dataset.
- load_algo(directory)
Load a machine learning algorithm from a directory.
- Parameters
directory (str | 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 (str | None) –
The name of the directory to save the algorithm.
By default it is set to None.
path (str | 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
- property is_trained: bool
Return whether the algorithm is trained.
- property learning_samples_indices: Sequence[int]
The indices of the learning samples used for the training.