gemseo / mlearning / clustering

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clustering module

This module contains the base classes for clustering algorithms.

The clustering 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.clustering.clustering.MLClusteringAlgo(data, transformer=mappingproxy({}), var_names=None, **parameters)

Bases: MLUnsupervisedAlgo

Clustering algorithm.

The 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, the Transformer will be applied to all the variables of this group. If IDENTITY, 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.

algo: Any

The interfaced machine learning algorithm.

input_names: list[str]

The names of the variables.

labels: list[int]

The indices of the clusters for the different samples.

learning_set: Dataset

The learning dataset.

n_clusters: int

The number of clusters.

parameters: dict[str, MLAlgoParameterType]

The parameters of the machine learning algorithm.

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) where resampler is a Resampler, ml_algos is the list of the associated machine learning algorithms built during the resampling stage and predictions 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, the Transformer will be applied to all the variables of this group.

class gemseo.mlearning.clustering.clustering.MLPredictiveClusteringAlgo(data, transformer=mappingproxy({}), var_names=None, **parameters)

Bases: MLClusteringAlgo

Predictive clustering algorithm.

The inheriting classes shall overload the MLUnsupervisedAlgo._fit() method, and the MLClusteringAlgo._predict() and MLClusteringAlgo._predict_proba() methods if possible.

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, the Transformer will be applied to all the variables of this group. If IDENTITY, 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.

predict(data)

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)

Predict the probability of belonging to each cluster from 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 dimension of the output array will be consistent with the dimension of the input arrays.

Parameters:

• data (ndarray | Mapping[str, 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:

ndarray

DataFormatters: ClassVar[type[BaseDataFormatters]]

The data formatters for the learning and prediction methods.

algo: Any

The interfaced machine learning algorithm.

input_names: list[str]

The names of the variables.

labels: list[int]

The indices of the clusters for the different samples.

learning_set: Dataset

The learning dataset.

n_clusters: int

The number of clusters.

parameters: dict[str, MLAlgoParameterType]

The parameters of the machine learning algorithm.

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) where resampler is a Resampler, ml_algos is the list of the associated machine learning algorithms built during the resampling stage and predictions 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, the Transformer will be applied to all the variables of this group.