pyemma.coordinates.clustering.KmeansClustering

class pyemma.coordinates.clustering.KmeansClustering(*args, **kwargs)

k-means clustering

__init__(n_clusters, max_iter=5, metric='euclidean', tolerance=1e-05, init_strategy='kmeans++', fixed_seed=False, oom_strategy='memmap', stride=1, n_jobs=None, skip=0, clustercenters=None, keep_data=False)

Kmeans clustering

Parameters
  • n_clusters (int) – amount of cluster centers. When not specified (None), min(sqrt(N), 5000) is chosen as default value, where N denotes the number of data points

  • max_iter (int) – maximum number of iterations before stopping.

  • tolerance (float) –

    stop iteration when the relative change in the cost function

    \[C(S) = \sum_{i=1}^{k} \sum_{\mathbf x \in S_i} \left\| \mathbf x - \boldsymbol\mu_i \right\|^2\]

    is smaller than tolerance.

  • metric (str) – metric to use during clustering (‘euclidean’, ‘minRMSD’)

  • init_strategy (string) – can be either ‘kmeans++’ or ‘uniform’, determining how the initial cluster centers are being chosen

  • fixed_seed (bool or int) – if True, the seed gets set to 42. Use time based seeding otherwise. if an integer is given, use this to initialize the random generator.

  • oom_strategy (string, default='memmap') –

    how to deal with out of memory situation during accumulation of all data.

    • ’memmap’: if no memory is available to store all data, a memory

      mapped file is created and written to

    • ’raise’: raise OutOfMemory exception.

  • stride (int) – stridden data

  • n_jobs (int or None, default None) – Number of threads to use during assignment of the data. If None, all available CPUs will be used.

  • clustercenters (None or array(k, dim)) – This is used to resume the kmeans iteration. Note, that if this is set, the init_strategy is ignored and the centers are directly passed to the kmeans iteration algorithm.

  • keep_data (boolean, default False) – If you intend to resume the kmeans iteration later on, in case it did not converge, this parameter controls whether the input data is kept in memory or not.

Methods

_Loggable__create_logger()

_ProgressReporterMixin__check_stage_registered(stage)

_SerializableMixIn__interpolate(state, klass)

__delattr__(name, /)

Implement delattr(self, name).

__dir__()

Default dir() implementation.

__eq__(value, /)

Return self==value.

__format__(format_spec, /)

Default object formatter.

__ge__(value, /)

Return self>=value.

__getattribute__(name, /)

Return getattr(self, name).

__getstate__()

__gt__(value, /)

Return self>value.

__hash__()

Return hash(self).

__init__(n_clusters[, max_iter, metric, …])

Kmeans clustering

__init_subclass__(*args, **kwargs)

This method is called when a class is subclassed.

__iter__()

__le__(value, /)

Return self<=value.

__lt__(value, /)

Return self<value.

__my_getstate__()

__my_setstate__(state)

__ne__(value, /)

Return self!=value.

__new__(cls, *args, **kwargs)

Create and return a new object.

__reduce__()

Helper for pickle.

__reduce_ex__(protocol, /)

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__setstate__(state)

__sizeof__()

Size of object in memory, in bytes.

__str__()

Return str(self).

__subclasshook__

Abstract classes can override this to customize issubclass().

_check_estimated()

_check_resume_iteration()

_chunk_finite(data)

_cleanup_logger(logger_id, logger_name)

_clear_in_memory()

_collect_data(X, first_chunk, last_chunk)

_compute_default_cs(dim, itemsize[, logger])

_create_iterator([skip, chunk, stride, …])

Should be implemented by non-abstract subclasses.

_data_flow_chain()

Get a list of all elements in the data flow graph.

_estimate(iterable, **kw)

_finish_estimate()

_get_classes_to_inspect()

gets classes self derives from which 1.

_get_interpolation_map(cls)

_get_model_param_names()

Get parameter names for the model

_get_param_names()

Get parameter names for the estimator

_get_private_field(cls, name[, default])

_get_serialize_fields(cls)

_get_state_of_serializeable_fields(klass, state)

:return a dictionary {k:v} for k in self.serialize_fields and v=getattr(self, k)

_get_traj_info(filename)

_get_version(cls[, require])

_get_version_for_class_from_state(state, klass)

retrieves the version of the current klass from the state mapping from old locations to new ones.

_init_estimate()

_init_in_memory_chunks(size)

_initialize_centers(X, itraj, t, last_chunk)

_logger_is_active(level)

@param level: int log level (debug=10, info=20, warn=30, error=40, critical=50)

_map_to_memory([stride])

Maps results to memory.

_progress_context([stage])

param stage

_progress_force_finish([stage, description])

forcefully finish the progress for given stage

_progress_register(amount_of_work[, …])

Registers a progress which can be reported/displayed via a progress bar.

_progress_set_description(stage, description)

set description of an already existing progress

_progress_update(numerator_increment[, …])

Updates the progress.

_set_random_access_strategies()

_set_state_from_serializeable_fields_and_state(…)

set only fields from state, which are present in klass.__serialize_fields

_source_from_memory([data_producer])

_transform_array(X)

get closest index of point in clustercenters to x.

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()

Get a descriptive string representation of this class.

dimension()

output dimension of clustering algorithm (always 1).

estimate(X, **kwargs)

Estimates the model given the data X

fit(X[, y])

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])

Maps all input data of this transformer and returns it as an array or list of arrays

get_params([deep])

Get parameters for this estimator.

iterator([stride, lag, chunk, …])

creates an iterator to stream over the (transformed) data.

load(file_name[, model_name])

Loads a previously saved PyEMMA object from disk.

n_chunks(chunksize[, stride, skip])

how many chunks an iterator of this sourcde will output, starting (eg.

n_frames_total([stride, skip])

Returns total number of frames.

number_of_trajectories([stride])

Returns the number of trajectories.

output_type()

By default transformers return single precision floats.

sample_indexes_by_cluster(clusters, nsample)

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

save(file_name[, model_name, overwrite, …])

saves the current state of this object to given file and name.

save_dtrajs([trajfiles, prefix, output_dir, …])

saves calculated discrete trajectories.

set_model_params(clustercenters)

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

write_to_hdf5(filename[, group, …])

writes all data of this Iterable to a given HDF5 file.

Attributes

_AbstractClustering__serialize_fields

_AbstractClustering__serialize_version

_DataSource__serialize_fields

_Estimator__serialize_fields

_FALLBACK_CHUNKSIZE

_InMemoryMixin__serialize_fields

_InMemoryMixin__serialize_version

_KmeansClustering__serialize_fields

_KmeansClustering__serialize_version

_Loggable__ids

_Loggable__refs

_SerializableMixIn__serialize_fields

_SerializableMixIn__serialize_modifications_map

_SerializableMixIn__serialize_version

__abstractmethods__

__dict__

__doc__

__module__

__weakref__

list of weak references to the object (if defined)

_abc_impl

_estimated

_estimator_type

_loglevel_CRITICAL

_loglevel_DEBUG

_loglevel_ERROR

_loglevel_INFO

_loglevel_WARN

_pg_threshold

_prog_rep_callbacks

_prog_rep_descriptions

_prog_rep_progressbars

_progress_num_registered

_progress_registered_stages

_save_data_producer

_serialize_version

chunksize

chunksize defines how much data is being processed at once.

cluster_centers_

Array containing the coordinates of the calculated cluster centers.

clustercenters

Array containing the coordinates of the calculated cluster centers.

converged

data_producer

The data producer for this data source object (can be another data source object).

default_chunksize

How much data will be processed at once, in case no chunksize has been provided.

dtrajs

Discrete trajectories (assigned data to cluster centers).

filenames

list of file names the data is originally being read from.

fixed_seed

seed for random choice of initial cluster centers.

in_memory

are results stored in memory?

index_clusters

Returns trajectory/time indexes for all the clusters

init_strategy

Strategy to get an initial guess for the centers.

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

n_jobs

Returns number of jobs/threads to use during assignment of data.

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

whether to show the progress of heavy calculations on this object.