Clustering¶

K-Means Clustering¶

class graspy.cluster.KMeansCluster(max_clusters=2, random_state=None)[source]¶

KMeans Cluster.

It computes all possible models from one component to max_clusters. The best model is given by the lowest silhouette score.

Parameters:

max_clusters : int, defaults to 1.: The maximum number of mixture components to consider.
random_state : int, RandomState instance or None, optional (default=None): If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

Attributes:

n_clusters_ : int: Optimal number of components. If y is given, it is based on largest ARI. Otherwise, it is based on smallest loss.
model_ : KMeans object: Fitted KMeans object fitted with optimal n_components.
silhouette_ : list: List of silhouette scores computed for all possible number of clusters given by range(2, max_clusters).
ari_ : list: Only computed when y is given. List of ARI values computed for all possible number of clusters given by range(2, max_clusters).

fit(self, X, y=None)[source]¶

Fits kmeans model to the data.

Parameters:	X : array-like, shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point. y : array-like, shape (n_samples,), optional (default=None) List of labels for X if available. Used to compute ARI scores.
Returns:	self

fit_predict(self, X, y=None)¶

Fit the models and predict clusters based on best model.

Parameters:	X : array-like, shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point. y : array-like, shape (n_samples,), optional (default=None) List of labels for X if available. Used to compute ARI scores.
Returns:	labels : array, shape (n_samples,) Component labels. ari : float Adjusted Rand index. Only returned if y is given.

get_params(self, deep=True)¶

Get parameters for this estimator.

Parameters:	deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping of string to any Parameter names mapped to their values.

predict(self, X, y=None)¶

Predict clusters based on best model.

Parameters:	X : array-like, shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point. y : array-like, shape (n_samples, ), optional (default=None) List of labels for X if available. Used to compute ARI scores.
Returns:	labels : array, shape (n_samples,) Component labels. ari : float Adjusted Rand index. Only returned if y is given.

set_params(self, **params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it's possible to update each component of a nested object.

Returns:	self

Gaussian Mixture Models Clustering¶

class graspy.cluster.GaussianCluster(min_components=2, max_components=None, covariance_type='full', random_state=None)[source]¶

Gaussian Mixture Model (GMM)

Representation of a Gaussian mixture model probability distribution. This class allows to estimate the parameters of a Gaussian mixture distribution. It computes all possible models from one component to max_components. The best model is given by the lowest BIC score.

Parameters:

min_components : int, default=2.

The minimum number of mixture components to consider (unless max_components=None, in which case this is the maximum number of components to consider). If max_componens is not None, min_components must be less than or equal to max_components.

max_components : int or None, default=None.

The maximum number of mixture components to consider. Must be greater than or equal to min_components.

covariance_type : {'full' (default), 'tied', 'diag', 'spherical'}, optional

String or list/array describing the type of covariance parameters to use. If a string, it must be one of:

'full'

each component has its own general covariance matrix
'tied'

all components share the same general covariance matrix
'diag'

each component has its own diagonal covariance matrix
'spherical'

each component has its own single variance
'all'

considers all covariance structures in ['spherical', 'diag', 'tied', 'full']

If a list/array, it must be a list/array of strings containing only: 'spherical', 'tied', 'diag', and/or 'spherical'.

random_state : int, RandomState instance or None, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.