gaussian_mixture module¶
The Gaussian mixture algorithm for clustering.
The Gaussian mixture algorithm groups the data into clusters. The number of clusters is fixed. Each cluster \(i=1, \\cdots, k\) is defined by a mean \(\\mu_i\) and a covariance matrix \(\\Sigma_i\).
The prediction of the cluster value of a point is simply the cluster where the probability density of the Gaussian distribution defined by the given mean and covariance matrix is the highest:
where \(\\mathcal{N}(x; \\mu_i, \\Sigma_i)\) is the value of the probability density function of a Gaussian random variable \(X \\sim \\mathcal{N}(\\mu_i, \\Sigma_i)\) at the point \(x\) and \(\\|x-\\mu_i\\|_{\\Sigma_i^{-1}} = \\sqrt{(x-\\mu_i)^T \\Sigma_i^{-1} (x-\\mu_i)}\) is the Mahalanobis distance between \(x\) and \(\\mu_i\) weighted by \(\\Sigma_i\). Likewise, the probability of belonging to a cluster \(i=1, \\cdots, k\) may be determined through
where \(C_i = \\{x\\, | \\, \\operatorname{cluster}(x) = i \\}\).
When fitting the algorithm, the cluster centers \(\\mu_i\) and the covariance matrices \(\\Sigma_i\) are computed using the expectation-maximization algorithm.
This concept is implemented through the GaussianMixture
class
which inherits from the MLClusteringAlgo
class.
Dependence¶
This clustering algorithm relies on the GaussianMixture class of the scikit-learn library.
- class gemseo.mlearning.clustering.gaussian_mixture.GaussianMixture(data, transformer=mappingproxy({}), var_names=None, n_components=5, random_state=0, **parameters)[source]¶
Bases:
MLPredictiveClusteringAlgo
The Gaussian mixture clustering algorithm.
- 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.n_components (int) –
The number of components of the Gaussian mixture.
By default it is set to 5.
random_state (int | None) –
The random state passed to the random number generator. Use an integer for reproducible results.
By default it is set to 0.
**parameters (int | float | str | bool | None) – 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.
- 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)¶
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.
- 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.
- 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:
- 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.
- IDENTITY: Final[DefaultTransformerType] = mappingproxy({})¶
A transformer leaving the input and output variables as they are.
- LIBRARY: Final[str] = 'scikit-learn'¶
The name of the library of the wrapped machine learning algorithm.
- SHORT_ALGO_NAME: ClassVar[str] = 'GMM'¶
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.
- property learning_samples_indices: Sequence[int]¶
The indices of the learning samples used for the training.
- 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.