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.
- class gemseo.mlearning.core.unsupervised.MLUnsupervisedAlgo(data, transformer=mappingproxy({}), var_names=None, **parameters)[source]
Bases:
MLAlgo
Unsupervised machine learning algorithm.
Inheriting classes shall overload the
MLUnsupervisedAlgo._fit()
method.- Parameters:
data (Dataset) – The learning dataset.
transformer (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, theTransformer
will be applied to all the variables of this group. IfIDENTITY
, do not transform the variables.By default it is set to {}.
var_names (Iterable[str] | None) – The names of the variables. If
None
, consider all variables mentioned in the learning dataset.**parameters (MLAlgoParameterType) – The parameters of the machine learning algorithm.
- Raises:
ValueError – When both the variable and the group it belongs to have a transformer.
- DataFormatters: ClassVar[type[BaseDataFormatters]]
The data formatters for the learning and prediction methods.
- SHORT_ALGO_NAME: ClassVar[str] = 'MLUnsupervisedAlgo'
The short name of the machine learning algorithm, often an acronym.
Typically used for composite names, e.g.
f"{algo.SHORT_ALGO_NAME}_{dataset.name}"
orf"{algo.SHORT_ALGO_NAME}_{discipline.name}"
.
- algo: Any
The interfaced machine learning algorithm.
- learning_set: Dataset
The learning dataset.
- resampling_results: dict[str, tuple[Resampler, list[MLAlgo], list[ndarray] | ndarray]]
The resampler class names bound to the resampling results.
A resampling result is formatted as
(resampler, ml_algos, predictions)
whereresampler
is aResampler
,ml_algos
is the list of the associated machine learning algorithms built during the resampling stage andpredictions
are the predictions obtained with the latter.resampling_results
stores only one resampling result per resampler type (e.g.,"CrossValidation"
,"LeaveOneOut"
and"Boostrap"
).
- transformer: dict[str, Transformer]
The strategies to transform the variables, if any.
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.