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:
|
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.- learning_set
The learning dataset.
- Type
- 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, theTransformer
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, 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 (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:
Decorators for the internal MLAlgo methods.
Attributes:
Return whether the algorithm is trained.
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