msmbuilder.cluster.Ward¶

class msmbuilder.cluster.Ward(*args, **kwargs)¶

Agglomerative Clustering

Recursively merges the pair of clusters that minimally increases a given linkage distance.

Read more in the User Guide.

Parameters:

n_clusters : int, default=2

The number of clusters to find.

connectivity : array-like or callable, optional

Connectivity matrix. Defines for each sample the neighboring samples following a given structure of the data. This can be a connectivity matrix itself or a callable that transforms the data into a connectivity matrix, such as derived from kneighbors_graph. Default is None, i.e, the hierarchical clustering algorithm is unstructured.

affinity : string or callable, default: “euclidean”

Metric used to compute the linkage. Can be “euclidean”, “l1”, “l2”, “manhattan”, “cosine”, or ‘precomputed’. If linkage is “ward”, only “euclidean” is accepted.

memory : Instance of joblib.Memory or string (optional)

Used to cache the output of the computation of the tree. By default, no caching is done. If a string is given, it is the path to the caching directory.

n_components : int (optional)

Number of connected components. If None the number of connected components is estimated from the connectivity matrix. NOTE: This parameter is now directly determined from the connectivity matrix and will be removed in 0.18

compute_full_tree : bool or ‘auto’ (optional)

Stop early the construction of the tree at n_clusters. This is useful to decrease computation time if the number of clusters is not small compared to the number of samples. This option is useful only when specifying a connectivity matrix. Note also that when varying the number of clusters and using caching, it may be advantageous to compute the full tree.

linkage : {“ward”, “complete”, “average”}, optional, default: “ward”

Which linkage criterion to use. The linkage criterion determines which distance to use between sets of observation. The algorithm will merge the pairs of cluster that minimize this criterion.

ward minimizes the variance of the clusters being merged.

average uses the average of the distances of each observation of the two sets.

complete or maximum linkage uses the maximum distances between all observations of the two sets.

pooling_func : callable, default=np.mean

This combines the values of agglomerated features into a single value, and should accept an array of shape [M, N] and the keyword argument axis=1, and reduce it to an array of size [M].

Attributes

labels_	(list of arrays, each of shape [sequence_length, ]) The label of each point is an integer in [0, n_clusters).
n_leaves_	(int) Number of leaves in the hierarchical tree.
n_components_	(int) The estimated number of connected components in the graph.
children_	(array-like, shape (n_nodes-1, 2)) The children of each non-leaf node. Values less than n_samples correspond to leaves of the tree which are the original samples. A node i greater than or equal to n_samples is a non-leaf node and has children children_[i - n_samples]. Alternatively at the i-th iteration, children[i][0] and children[i][1] are merged to form node n_samples + i

Methods

`fit`(sequences[, y])	Fit the clustering on the data
`fit_predict`(sequences[, y])	Performs clustering on X and returns cluster labels.
`fit_transform`(sequences[, y])	Alias for fit_predict
`get_params`([deep])	Get parameters for this estimator.
`partial_predict`(X[, y])	Predict the closest cluster each sample in X belongs to.
`partial_transform`(X)	Alias for partial_predict
`predict`(sequences[, y])	Predict the closest cluster each sample in each sequence in sequences belongs to.
`set_params`(**params)	Set the parameters of this estimator.
`summarize`()	Return some diagnostic summary statistics about this Markov model
`transform`(sequences)	Alias for predict

__init__(*args, **kwargs)¶

Methods

`__init__`(args, *kwargs)
`fit`(sequences[, y])	Fit the clustering on the data
`fit_predict`(sequences[, y])	Performs clustering on X and returns cluster labels.
`fit_transform`(sequences[, y])	Alias for fit_predict
`get_params`([deep])	Get parameters for this estimator.
`partial_predict`(X[, y])	Predict the closest cluster each sample in X belongs to.
`partial_transform`(X)	Alias for partial_predict
`predict`(sequences[, y])	Predict the closest cluster each sample in each sequence in sequences belongs to.
`set_params`(**params)	Set the parameters of this estimator.
`summarize`()	Return some diagnostic summary statistics about this Markov model
`transform`(sequences)	Alias for predict

fit(sequences, y=None)¶

Fit the clustering on the data

Parameters:

sequences : list of array-like, each of shape [sequence_length, n_features]

A list of multivariate timeseries. Each sequence may have a different length, but they all must have the same number of features.

Returns:

self

fit_predict(sequences, y=None)¶

Performs clustering on X and returns cluster labels.

Parameters:

sequences : list of array-like, each of shape [sequence_length, n_features]

A list of multivariate timeseries. Each sequence may have a different length, but they all must have the same number of features.

Returns:

Y : list of ndarray, each of shape [sequence_length, ]

Cluster labels

fit_transform(sequences, y=None)¶: Alias for fit_predict

get_params(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.

partial_predict(X, y=None)¶

Predict the closest cluster each sample in X belongs to.

In the vector quantization literature, cluster_centers_ is called the code book and each value returned by predict is the index of the closest code in the code book.

Parameters:

X : array-like shape=(n_samples, n_features)

A single timeseries.

Returns:

Y : array, shape=(n_samples,)

Index of the cluster that each sample belongs to

partial_transform(X)¶: Alias for partial_predict

predict(sequences, y=None)¶

Predict the closest cluster each sample in each sequence in sequences belongs to.

In the vector quantization literature, cluster_centers_ is called the code book and each value returned by predict is the index of the closest code in the code book.

Parameters:

sequences : list of array-like, each of shape [sequence_length, n_features]

A list of multivariate timeseries. Each sequence may have a different length, but they all must have the same number of features.

Returns:

Y : list of arrays, each of shape [sequence_length,]

Index of the closest center each sample belongs to.

set_params(**params)¶

Set the parameters of this estimator.

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

Returns:	self

summarize()¶: Return some diagnostic summary statistics about this Markov model

transform(sequences)¶: Alias for predict