Clustering models options

GaussianMixture

class gemseo.mlearning.cluster.gaussian_mixture.GaussianMixture(data, transformer=None, var_names=None, n_components=5, **parameters)

The Gaussian mixture clustering algorithm.

Parameters

• n_components (int) – The number of components of the Gaussian mixture.

• data (Dataset) –

• transformer (Optional[TransformerType]) –

• var_names (Optional[Iterable[str]]) –

• parameters (Optional[Union[int,float,str,bool]]) –

Return type

None

Classes:

DataFormatters()

Decorators for the internal MLAlgo methods.

Attributes:

is_trained

Return whether the algorithm is trained.

Methods:

learn([samples])	Train the machine learning algorithm from the learning dataset.
load_algo(directory)	Load a machine learning algorithm from a directory.
predict(data)	Predict the clusters from the input data.
predict_proba(data[, hard])	Predict the probability of belonging to each cluster from input data.
save([directory, path, save_learning_set])	Save the machine learning algorithm.

class DataFormatters

Decorators for the internal MLAlgo methods.

property is_trained

Return whether the algorithm is trained.

learn(samples=None)

Train the machine learning algorithm from the learning dataset.

Parameters

samples (Optional[List[int]]) – The indices of the learning samples. If None, use the whole learning dataset.

Return type

None

load_algo(directory)

Load a machine learning algorithm from a directory.

Parameters

directory (str) – 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 (Union[numpy.ndarray, Dict[str, numpy.ndarray]]) – The input data.

Returns

The predicted cluster for each input data sample.

Return type

Union[int, numpy.ndarray]

predict_proba(data, hard=True)

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, Dict[str, numpy.ndarray]]) – The input data.

• hard (bool) – Whether clustering should be hard (True) or soft (False).

Returns

The probability of belonging to each cluster, with shape (n_samples, n_clusters) or (n_clusters,).

Return type

numpy.ndarray

save(directory=None, path='.', save_learning_set=False)

Save the machine learning algorithm.

Parameters

• directory (Optional[str]) – The name of the directory to save the algorithm.

• path (str) – The path to parent directory where to create the directory.

• save_learning_set (bool) – If False, do not save the learning set to lighten the saved files.

Returns

The path to the directory where the algorithm is saved.

Return type

str

KMeans

class gemseo.mlearning.cluster.kmeans.KMeans(data, transformer=None, var_names=None, n_clusters=5, random_state=0, **parameters)

The k-means clustering algorithm.

Parameters

• n_clusters (int) – The number of clusters of the K-means algorithm.

• random_state (Optional[int]) – If None, use a random generation of the initial centroids. If not None, the integer is used to make the initialization deterministic.

• data (Dataset) –

• transformer (Optional[TransformerType]) –

• var_names (Optional[Iterable[str]]) –

• parameters (Optional[Union[int,float,bool,str]]) –

Return type

None

Classes:

DataFormatters()

Decorators for the internal MLAlgo methods.

Attributes:

is_trained

Return whether the algorithm is trained.

Methods:

learn([samples])	Train the machine learning algorithm from the learning dataset.
load_algo(directory)	Load a machine learning algorithm from a directory.
predict(data)	Predict the clusters from the input data.
predict_proba(data[, hard])	Predict the probability of belonging to each cluster from input data.
save([directory, path, save_learning_set])	Save the machine learning algorithm.

class DataFormatters

Decorators for the internal MLAlgo methods.

property is_trained

Return whether the algorithm is trained.

learn(samples=None)

Train the machine learning algorithm from the learning dataset.

Parameters

samples (Optional[List[int]]) – The indices of the learning samples. If None, use the whole learning dataset.

Return type

None

load_algo(directory)

Load a machine learning algorithm from a directory.

Parameters

directory (str) – 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 (Union[numpy.ndarray, Dict[str, numpy.ndarray]]) – The input data.

Returns

The predicted cluster for each input data sample.

Return type

Union[int, numpy.ndarray]

predict_proba(data, hard=True)

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, Dict[str, numpy.ndarray]]) – The input data.

• hard (bool) – Whether clustering should be hard (True) or soft (False).

Returns

The probability of belonging to each cluster, with shape (n_samples, n_clusters) or (n_clusters,).

Return type

numpy.ndarray

save(directory=None, path='.', save_learning_set=False)

Save the machine learning algorithm.

Parameters

• directory (Optional[str]) – The name of the directory to save the algorithm.

• path (str) – The path to parent directory where to create the directory.

• save_learning_set (bool) – If False, do not save the learning set to lighten the saved files.

Returns

The path to the directory where the algorithm is saved.

Return type

str

MLPredictiveClusteringAlgo

class gemseo.mlearning.cluster.cluster.MLPredictiveClusteringAlgo(data, transformer=None, var_names=None, **parameters)

Predictive clustering algorithm.

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

Attributes

• learning_set (Dataset) – The learning dataset.

• parameters (Dict[str,MLAlgoParameterType]) – The parameters of the machine learning algorithm.

• transformer (Dict[str,Transformer]) – 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 None, do not transform the variables.

• algo (Any) – The interfaced machine learning algorithm.

• learning_set (Dataset) – The learning dataset.

• parameters (Dict[str,MLAlgoParameterType]) – The parameters of the machine learning algorithm.

• transformer (Dict[str,Transformer]) – 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 None, do not transform the variables.

• algo (Any) – The interfaced machine learning algorithm.

• input_names (List[str]) – The names of the variables.

• learning_set (Dataset) – The learning dataset.

• parameters (Dict[str,MLAlgoParameterType]) – The parameters of the machine learning algorithm.

• transformer (Dict[str,Transformer]) – 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 None, do not transform the variables.

• algo (Any) – The interfaced machine learning algorithm.

• learning_set (Dataset) – The learning dataset.

• parameters (Dict[str,MLAlgoParameterType]) – The parameters of the machine learning algorithm.

• transformer (Dict[str,Transformer]) – 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 None, do not transform the variables.

• 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.

• n_clusters (int) – The number of clusters.

Parameters

• data (Dataset) –

• transformer (Optional[TransformerType]) –

• var_names (Optional[Iterable[str]]) –

• parameters (MLAlgoParameterType) –

Return type

None

Parameters: data: The learning dataset. transformer: 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 None, do not transform the variables.

**parameters: The parameters of the machine learning algorithm. var_names: The names of the variables.

If None, consider all variables mentioned in the learning dataset.

Classes:

DataFormatters()

Decorators for the internal MLAlgo methods.

Attributes:

is_trained

Return whether the algorithm is trained.

Methods:

learn([samples])	Train the machine learning algorithm from the learning dataset.
load_algo(directory)	Load a machine learning algorithm from a directory.
predict(data)	Predict the clusters from the input data.
predict_proba(data[, hard])	Predict the probability of belonging to each cluster from input data.
save([directory, path, save_learning_set])	Save the machine learning algorithm.

class DataFormatters

Decorators for the internal MLAlgo methods.

property is_trained

Return whether the algorithm is trained.

learn(samples=None)

Train the machine learning algorithm from the learning dataset.

Parameters

samples (Optional[List[int]]) – The indices of the learning samples. If None, use the whole learning dataset.

Return type

None

load_algo(directory)

Load a machine learning algorithm from a directory.

Parameters

directory (str) – 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 (Union[numpy.ndarray, Dict[str, numpy.ndarray]]) – The input data.

Returns

The predicted cluster for each input data sample.

Return type

Union[int, numpy.ndarray]

predict_proba(data, hard=True)

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, Dict[str, numpy.ndarray]]) – The input data.

• hard (bool) – Whether clustering should be hard (True) or soft (False).

Returns

The probability of belonging to each cluster, with shape (n_samples, n_clusters) or (n_clusters,).

Return type

numpy.ndarray

save(directory=None, path='.', save_learning_set=False)

Save the machine learning algorithm.

Parameters

• directory (Optional[str]) – The name of the directory to save the algorithm.

• path (str) – The path to parent directory where to create the directory.

• save_learning_set (bool) – If False, do not save the learning set to lighten the saved files.

Returns

The path to the directory where the algorithm is saved.

Return type

str