kmeans module¶
The k-means algorithm for clustering.
The k-means algorithm groups the data into clusters, where the number of clusters \(k\) is fixed. This is done by initializing \(k\) centroids in the design space. The points are grouped into clusters according to their nearest centroid.
When fitting the algorithm, each centroid is successively moved to the mean of its corresponding cluster, and the cluster value of each point is then reset to the cluster value of the closest centroid. This process is repeated until convergence.
Cluster values of new points may be predicted by returning the value of the closest centroid. Denoting \((c_1, \cdots, c_k) \in \mathbb{R}^{n \times k}\) the centroids, and assuming no overlap between the centroids, we may compute the prediction
A probability measure may also be provided, using the distances from the point to each of the centroids:
where \(C_i = \{x\, | \, \operatorname{cluster}(x) = i \}\). Here, \(\mathbb{P}(x \in C_i)\) represents the probability of cluster \(i\) given the point \(x\).
This concept is implemented through the KMeans
class
which inherits from the MLClusteringAlgo
class.
Dependence¶
This clustering algorithm relies on the KMeans class of the scikit-learn library.
- class gemseo.mlearning.clustering.kmeans.KMeans(data, transformer=mappingproxy({}), var_names=None, n_clusters=5, random_state=0, **parameters)[source]¶
Bases:
MLPredictiveClusteringAlgo
The k-means 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_clusters (int) –
The number of clusters of the K-means algorithm.
By default it is set to 5.
random_state (int | None) –
If
None
, use a random generation of the initial centroids. Otherwise, the integer is used to make the initialization deterministic.By default it is set to 0.
**parameters (int | float | bool | str | 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.
- 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.
- 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.
- EPS = 2.220446049250313e-16¶
- 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] = 'KMeans'¶
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.
- 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.