Clustering¶
This module contains the base classes for clustering algorithms.
The cluster
module
implements the concept of clustering models,
a kind of unsupervised machine learning algorithm
where the goal is to group data into clusters.
Wherever possible,
these methods should be able to predict the class of the new data,
as well as the probability of belonging to each class.
This concept is implemented
through the MLClusteringAlgo
class,
which inherits from the MLUnsupervisedAlgo
class,
and through the MLPredictiveClusteringAlgo
class
which inherits from MLClusteringAlgo
.
- class gemseo.mlearning.cluster.cluster.MLClusteringAlgo(data, transformer=None, var_names=None, **parameters)[source]
Clustering algorithm.
The 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.
- 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.
- n_clusters: int
The number of clusters.
- class gemseo.mlearning.cluster.cluster.MLPredictiveClusteringAlgo(data, transformer=None, var_names=None, **parameters)[source]
Predictive clustering algorithm.
The inheriting classes shall overload the
MLUnsupervisedAlgo._fit()
method, and theMLClusteringAlgo._predict()
andMLClusteringAlgo._predict_proba()
methods if possible.- 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.
- 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
- predict(data)[source]
Predict the clusters from the input data.
The user can specify these input data either as a NumPy array, e.g.
array([1., 2., 3.])
or as a dictionary, e.g.{'a': array([1.]), 'b': array([2., 3.])}
.If the numpy arrays are of dimension 2, their i-th rows represent the input data of the i-th sample; while if the numpy arrays are of dimension 1, there is a single sample.
The type of the output data and the dimension of the output arrays will be consistent with the type of the input data and the dimension of the input arrays.
- Parameters
data (DataType) – The input data.
- Returns
The predicted cluster for each input data sample.
- Return type
int | ndarray
- predict_proba(data, hard=True)[source]
Predict the probability of belonging to each cluster from input data.
The user can specified these input data either as a numpy array, e.g.
array([1., 2., 3.])
or as a dictionary, e.g.{'a': array([1.]), 'b': array([2., 3.])}
.If the numpy arrays are of dimension 2, their i-th rows represent the input data of the i-th sample; while if the numpy arrays are of dimension 1, there is a single sample.
The dimension of the output array will be consistent with the dimension of the input arrays.
- Parameters
data (Union[numpy.ndarray, Mapping[str, numpy.ndarray]]) – The input data.
hard (bool) –
Whether clustering should be hard (True) or soft (False).
By default it is set to True.
- Returns
The probability of belonging to each cluster, with shape (n_samples, n_clusters) or (n_clusters,).
- Return type
- 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.
Available clustering models are: