pyemma.coordinates.clustering.MiniBatchKmeansClustering¶

class pyemma.coordinates.clustering.MiniBatchKmeansClustering(n_clusters, max_iter=5, metric='euclidean', tolerance=1e-05, init_strategy='kmeans++', batch_size=0.2, oom_strategy='memmap', fixed_seed=False, stride=None)¶

Mini-batch k-means clustering

__init__(n_clusters, max_iter=5, metric='euclidean', tolerance=1e-05, init_strategy='kmeans++', batch_size=0.2, oom_strategy='memmap', fixed_seed=False, stride=None)¶

Methods

`__init__`(n_clusters[, max_iter, metric, ...])
`assign`([X, stride])	Assigns the given trajectory or list of trajectories to cluster centers by using the discretization defined by this clustering method (usually a Voronoi tesselation).
`describe`(args, *kwargs)	Get a descriptive string representation of this class.
`dimension`()	output dimension of clustering algorithm (always 1).
`estimate`(X, **kwargs)
`fit`(X)	Estimates parameters - for compatibility with sklearn.
`fit_predict`(X[, y])	Performs clustering on X and returns cluster labels.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_model_params`([deep])	Get parameters for this model.
`get_output`([dimensions, stride, skip, chunk])
`get_params`([deep])	Get parameters for this estimator.
`iterator`([stride, lag, chunk, ...])
`kmeanspp_center_assigned`()
`n_frames_total`([stride])	Returns total number of frames.
`number_of_trajectories`()	Returns the number of trajectories.
`output_type`()
`parametrize`([stride])
`register_progress_callback`(call_back[, stage])	Registers the progress reporter.
`sample_indexes_by_cluster`(clusters, nsample)	Samples trajectory/time indexes according to the given sequence of states.
`save_dtrajs`([trajfiles, prefix, output_dir, ...])	saves calculated discrete trajectories. Filenames are taken from
`set_params`(**params)	Set the parameters of this estimator.
`trajectory_length`(itraj[, stride, skip])	Returns the length of trajectory of the requested index.
`trajectory_lengths`([stride, skip])	Returns the length of each trajectory.
`transform`(X)	Maps the input data through the transformer to correspondingly shaped output data array/list.
`update_model_params`(**params)	Update given model parameter if they are set to specific values
`write_to_csv`([filename, extension, ...])	write all data to csv with numpy.savetxt

Attributes

`chunksize`
`clustercenters`	Array containing the coordinates of the calculated cluster centers.
`data_producer`
`default_chunksize`
`dtrajs`	Discrete trajectories (assigned data to cluster centers).
`filenames`	Property which returns a list of filenames the data is originally from.
`in_memory`	are results stored in memory?
`index_clusters`	Returns trajectory/time indexes for all the clusters
`is_random_accessible`	Check if self._is_random_accessible is set to true and if all the random access strategies are implemented.
`is_reader`	Property telling if this data source is a reader or not.
`logger`	The logger for this class instance
`model`	The model estimated by this Estimator
`name`	The name of this instance
`ndim`
`ntraj`
`overwrite_dtrajs`	Should existing dtraj files be overwritten.
`ra_itraj_cuboid`	Implementation of random access with slicing that can be up to 3-dimensional, where the first dimension corresponds to the trajectory index, the second dimension corresponds to the frames and the third dimension corresponds to the dimensions of the frames.
`ra_itraj_jagged`	Behaves like ra_itraj_cuboid just that the trajectories are not truncated and returned as a list.
`ra_itraj_linear`	Implementation of random access that takes arguments as the default random access (i.e., up to three dimensions with trajs, frames and dims, respectively), but which considers the frame indexing to be contiguous.
`ra_linear`	Implementation of random access that takes a (maximal) two-dimensional slice where the first component corresponds to the frames and the second component corresponds to the dimensions.
`show_progress`

assign(X=None, stride=1)¶

Assigns the given trajectory or list of trajectories to cluster centers by using the discretization defined by this clustering method (usually a Voronoi tesselation).

You can assign multiple times with different strides. The last result of assign will be saved and is available as the attribute dtrajs().

Parameters:	X (ndarray(T, n) or list of ndarray(T_i, n), optional, default = None) – Optional input data to map, where T is the number of time steps and n is the number of dimensions. When a list is provided they can have differently many time steps, but the number of dimensions need to be consistent. When X is not provided, the result of assign is identical to get_output(), i.e. the data used for clustering will be assigned. If X is given, the stride argument is not accepted. stride (int, optional, default = 1) – If set to 1, all frames of the input data will be assigned. Note that this could cause this calculation to be very slow for large data sets. Since molecular dynamics data is usually correlated at short timescales, it is often sufficient to obtain the discretization at a longer stride. Note that the stride option used to conduct the clustering is independent of the assign stride. This argument is only accepted if X is not given.
Returns:	Y – The discretized trajectory: int-array with the indexes of the assigned clusters, or list of such int-arrays. If called with a list of trajectories, Y will also be a corresponding list of discrete trajectories
Return type:	ndarray(T, dtype=int) or list of ndarray(T_i, dtype=int)

clustercenters¶: Array containing the coordinates of the calculated cluster centers.

describe(*args, **kwargs)¶: Get a descriptive string representation of this class.

dimension()¶: output dimension of clustering algorithm (always 1).

dtrajs¶: Discrete trajectories (assigned data to cluster centers).

filenames¶: Property which returns a list of filenames the data is originally from. :returns: list of str :rtype: list of filenames if data is originating from a file based reader

fit(X)¶

Estimates parameters - for compatibility with sklearn.

Parameters:	X (object) – A reference to the data from which the model will be estimated
Returns:	estimator – The estimator (self) with estimated model.
Return type:	object

fit_predict(X, y=None)¶

Performs clustering on X and returns cluster labels. :param X: Input data. :type X: ndarray, shape (n_samples, n_features)

Returns:	y – cluster labels
Return type:	ndarray, shape (n_samples,)

fit_transform(X, y=None, **fit_params)¶

Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. :param X: Training set. :type X: numpy array of shape [n_samples, n_features] :param y: Target values. :type y: numpy array of shape [n_samples]

Returns:	X_new – Transformed array.
Return type:	numpy array of shape [n_samples, n_features_new]

get_model_params(deep=True)¶

Get parameters for this model.

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

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 – Parameter names mapped to their values.
Return type:	mapping of string to any

in_memory¶: are results stored in memory?

index_clusters¶

Returns trajectory/time indexes for all the clusters

Returns:	indexes – For each state, all trajectory and time indexes where this cluster occurs. Each matrix has a number of rows equal to the number of occurrences of the corresponding state, with rows consisting of a tuple (i, t), where i is the index of the trajectory and t is the time index within the trajectory.
Return type:	list of ndarray( (N_i, 2) )

is_random_accessible¶: Check if self._is_random_accessible is set to true and if all the random access strategies are implemented. :returns: bool :rtype: Returns True if random accessible via strategies and False otherwise.

is_reader¶: Property telling if this data source is a reader or not. :returns: bool :rtype: True if this data source is a reader and False otherwise

logger¶: The logger for this class instance

model¶: The model estimated by this Estimator

n_frames_total(stride=1)¶

Returns total number of frames.

Parameters:	stride (int) – return value is the number of frames in trajectories when running through them with a step size of stride.
Returns:	int
Return type:	n_frames_total

name¶: The name of this instance

number_of_trajectories()¶

Returns the number of trajectories.

Returns:	int
Return type:	number of trajectories

overwrite_dtrajs¶: Should existing dtraj files be overwritten. Set this property to True to overwrite.

ra_itraj_cuboid¶

Implementation of random access with slicing that can be up to 3-dimensional, where the first dimension corresponds to the trajectory index, the second dimension corresponds to the frames and the third dimension corresponds to the dimensions of the frames.

The with the frame slice selected frames will be loaded from each in the trajectory-slice selected trajectories and then sliced with the dimension slice. For example: The data consists out of three trajectories with length 10, 20, 10, respectively. The slice data[:, :15, :3] returns a 3D array of shape (3, 10, 3), where the first component corresponds to the three trajectories, the second component corresponds to 10 frames (note that the last 5 frames are being truncated as the other two trajectories only have 10 frames) and the third component corresponds to the selected first three dimensions.

Returns:	Returns an object that allows access by slices in the described manner.

ra_itraj_jagged¶

Behaves like ra_itraj_cuboid just that the trajectories are not truncated and returned as a list.

Returns:	Returns an object that allows access by slices in the described manner.

ra_itraj_linear¶

Implementation of random access that takes arguments as the default random access (i.e., up to three dimensions with trajs, frames and dims, respectively), but which considers the frame indexing to be contiguous. Therefore, it returns a simple 2D array.

Returns:	A 2D array of the sliced data containing [frames, dims].

ra_linear¶

Implementation of random access that takes a (maximal) two-dimensional slice where the first component corresponds to the frames and the second component corresponds to the dimensions. Here it is assumed that the frame indexing is contiguous, i.e., the first frame of the second trajectory has the index of the last frame of the first trajectory plus one.

Returns:	Returns an object that allows access by slices in the described manner.

register_progress_callback(call_back, stage=0)¶

Registers the progress reporter.

Parameters:	call_back (function) – This function will be called with the following arguments: stage (int) instance of pyemma.utils.progressbar.ProgressBar optional args and named keywords (kw), for future changes stage* (int, optional, default=0) – The stage you want the given call back function to be fired.

sample_indexes_by_cluster(clusters, nsample, replace=True)¶

Samples trajectory/time indexes according to the given sequence of states.

Parameters:	clusters (iterable of integers) – It contains the cluster indexes to be sampled nsample (int) – Number of samples per cluster. If replace = False, the number of returned samples per cluster could be smaller if less than nsample indexes are available for a cluster. replace (boolean, optional) – Whether the sample is with or without replacement
Returns:	indexes – List of the sampled indices by cluster. Each element is an index array with a number of rows equal to N=len(sequence), with rows consisting of a tuple (i, t), where i is the index of the trajectory and t is the time index within the trajectory.
Return type:	list of ndarray( (N, 2) )

save_dtrajs(trajfiles=None, prefix='', output_dir='.', output_format='ascii', extension='.dtraj')¶

saves calculated discrete trajectories. Filenames are taken from given reader. If data comes from memory dtrajs are written to a default filename.

Parameters:

trajfiles (list of str (optional)) – names of input trajectory files, will be used generate output files.
prefix (str) – prepend prefix to filenames.
output_dir (str) – save files to this directory.
output_format (str) – if format is ‘ascii’ dtrajs will be written as csv files, otherwise they will be written as NumPy .npy files.
extension (str) – file extension to append (eg. ‘.itraj’)

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: :rtype: self

trajectory_length(itraj, stride=1, skip=None)¶

Returns the length of trajectory of the requested index.

Parameters:	itraj (int) – trajectory index stride (int) – return value is the number of frames in the trajectory when running through it with a step size of stride.
Returns:	int
Return type:	length of trajectory

trajectory_lengths(stride=1, skip=0)¶

Returns the length of each trajectory.

Parameters:	stride (int) – return value is the number of frames of the trajectories when running through them with a step size of stride. skip (int) – skip parameter
Returns:	array(dtype=int)
Return type:	containing length of each trajectory

transform(X)¶

Maps the input data through the transformer to correspondingly shaped output data array/list.

Parameters:	X (ndarray(T, n) or list of ndarray(T_i, n)) – The input data, where T is the number of time steps and n is the number of dimensions. If a list is provided, the number of time steps is allowed to vary, but the number of dimensions are required to be to be consistent.
Returns:	Y – The mapped data, where T is the number of time steps of the input data and d is the output dimension of this transformer. If called with a list of trajectories, Y will also be a corresponding list of trajectories
Return type:	ndarray(T, d) or list of ndarray(T_i, d)

update_model_params(**params)¶: Update given model parameter if they are set to specific values

write_to_csv(filename=None, extension='.dat', overwrite=False, stride=1, chunksize=100, **kw)¶

write all data to csv with numpy.savetxt

Parameters:

filename (str, optional) –
filename string, which may contain placeholders {itraj} and {stride}:
- itraj will be replaced by trajetory index
- stride is stride argument of this method
If filename is not given, it is being tried to obtain the filenames from the data source of this iterator.
extension (str, optional, default='.dat') – filename extension of created files
overwrite (bool, optional, default=False) – shall existing files be overwritten? If a file exists, this method will raise.
stride (int) – omit every n’th frame
chunksize (int) – how many frames to process at once
kw (dict) – named arguments passed into numpy.savetxt (header, seperator etc.)

Example

Assume you want to save features calculated by some FeatureReader to ASCII: >>> import numpy as np, pyemma >>> from pyemma.util.files import TemporaryDirectory >>> import os >>> data = [np.random.random((10,3))] * 3 >>> reader = pyemma.coordinates.source(data) >>> filename = “distances_{itraj}.dat” >>> with TemporaryDirectory() as td: ... os.chdir(td) ... reader.write_to_csv(filename, header=’‘, delim=’;’) ... print(os.listdir(‘.’)) [‘distances_2.dat’, ‘distances_1.dat’, ‘distances_0.dat’]