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 BaseMLClusteringAlgo class, which inherits from the BaseMLUnsupervisedAlgo class, and through the BaseMLPredictiveClusteringAlgo class, which inherits from BaseMLClusteringAlgo.

class gemseo.mlearning.clustering.clustering.BaseMLClusteringAlgo(data, transformer=mappingproxy({}), var_names=None, **parameters)[source]

Bases: BaseMLUnsupervisedAlgo

Clustering algorithm.

The inheriting classes shall overload the BaseMLUnsupervisedAlgo._fit() method.

Parameters:
  • data (Dataset) – The learning dataset.

  • transformer (TransformerType) –

    The strategies to transform the variables. The values are instances of BaseTransformer 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 BaseTransformer 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.

learn(samples=None, fit_transformers=True)

Train the machine learning algorithm from the learning dataset.

Parameters:
  • samples (Sequence[int] | None) – The indices of the learning samples. If None, use the whole learning dataset.

  • fit_transformers (bool) –

    Whether to fit the variable transformers. Otherwise, use them as they are.

    By default it is set to True.

Return type:

None

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

to_pickle(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.

  • 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:

str

DEFAULT_TRANSFORMER: DefaultTransformerType = mappingproxy({})

The default transformer for the input and output data, if any.

DataFormatters: ClassVar[type[BaseDataFormatters]]

The data formatters for the learning and prediction methods.

FILENAME: ClassVar[str] = 'ml_algo.pkl'
IDENTITY: Final[DefaultTransformerType] = mappingproxy({})

A transformer leaving the input and output variables as they are.

LIBRARY: ClassVar[str] = ''

The name of the library of the wrapped machine learning algorithm.

SHORT_ALGO_NAME: ClassVar[str] = 'BaseMLUnsupervisedAlgo'

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}" or f"{algo.SHORT_ALGO_NAME}_{discipline.name}".

algo: Any

The interfaced machine learning algorithm.

input_names: list[str]

The names of the variables.

property is_trained: bool

Return whether the algorithm is trained.

labels: list[int]

The indices of the clusters for the different samples.

property learning_samples_indices: Sequence[int]

The indices of the learning samples used for the training.

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[BaseResampler, list[BaseMLAlgo], 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 BaseResampler, 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, BaseTransformer]

The strategies to transform the variables, if any.

The values are instances of BaseTransformer 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 BaseTransformer will be applied to all the variables of this group.

class gemseo.mlearning.clustering.clustering.BaseMLPredictiveClusteringAlgo(data, transformer=mappingproxy({}), var_names=None, **parameters)[source]

Bases: BaseMLClusteringAlgo

Predictive clustering algorithm.

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

Parameters:
  • data (Dataset) – The learning dataset.

  • transformer (TransformerType) –

    The strategies to transform the variables. The values are instances of BaseTransformer 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 BaseTransformer 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.

learn(samples=None, fit_transformers=True)

Train the machine learning algorithm from the learning dataset.

Parameters:
  • samples (Sequence[int] | None) – The indices of the learning samples. If None, use the whole learning dataset.

  • fit_transformers (bool) –

    Whether to fit the variable transformers. Otherwise, use them as they are.

    By default it is set to True.

Return type:

None

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 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

to_pickle(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.

  • 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:

str

DEFAULT_TRANSFORMER: DefaultTransformerType = mappingproxy({})

The default transformer for the input and output data, if any.

DataFormatters: ClassVar[type[BaseDataFormatters]]

The data formatters for the learning and prediction methods.

FILENAME: ClassVar[str] = 'ml_algo.pkl'
IDENTITY: Final[DefaultTransformerType] = mappingproxy({})

A transformer leaving the input and output variables as they are.

LIBRARY: ClassVar[str] = ''

The name of the library of the wrapped machine learning algorithm.

SHORT_ALGO_NAME: ClassVar[str] = 'BaseMLUnsupervisedAlgo'

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}" or f"{algo.SHORT_ALGO_NAME}_{discipline.name}".

algo: Any

The interfaced machine learning algorithm.

input_names: list[str]

The names of the variables.

property is_trained: bool

Return whether the algorithm is trained.

labels: list[int]

The indices of the clusters for the different samples.

property learning_samples_indices: Sequence[int]

The indices of the learning samples used for the training.

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[BaseResampler, list[BaseMLAlgo], 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 BaseResampler, 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, BaseTransformer]

The strategies to transform the variables, if any.

The values are instances of BaseTransformer 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 BaseTransformer will be applied to all the variables of this group.