replay_trajectory_classification.classifier.ClusterlessClassifier#

class ClusterlessClassifier(environments: list[Environment] = Environment(environment_name='', place_bin_size=2.0, track_graph=None, edge_order=None, edge_spacing=None, is_track_interior=None, position_range=None, infer_track_interior=True, fill_holes=False, dilate=False, bin_count_threshold=0), observation_models=None, continuous_transition_types: list[list[EmpiricalMovement | RandomWalk | RandomWalkDirection1 | RandomWalkDirection2 | Uniform]] = [[RandomWalk(environment_name='', movement_var=6.0, movement_mean=0.0, use_diffusion=False), Uniform(environment_name='', environment2_name=None)], [Uniform(environment_name='', environment2_name=None), Uniform(environment_name='', environment2_name=None)]], discrete_transition_type: DiagonalDiscrete | RandomDiscrete | UniformDiscrete | UserDefinedDiscrete = DiagonalDiscrete(diagonal_value=0.98), initial_conditions_type: UniformInitialConditions | UniformOneEnvironmentInitialConditions = UniformInitialConditions(), infer_track_interior: bool = True, clusterless_algorithm: str = 'multiunit_likelihood', clusterless_algorithm_params: dict = {'mark_std': 24.0, 'position_std': 6.0})[source]#

Bases: _ClassifierBase

Classifies neural population representation of position and trajectory from multiunit spikes and waveforms.

Parameters:

environments (list of Environment instances, optional) – The spatial environment(s) to fit
observation_models (ObservationModel instance, optional) – Links environments and encoding group
continuous_transition_types (list of list of transition matrix instances, optional) – Types of transition models, by default _DEFAULT_CONTINUOUS_TRANSITIONS Length correspond to number of discrete states.
discrete_transition_type (discrete transition instance, optional)
initial_conditions_type (initial conditions instance, optional) – The initial conditions class instance
infer_track_interior (bool, optional) – Whether to infer the spatial geometry of track from position
clusterless_algorithm (str) – The type of clusterless algorithm. See _ClUSTERLESS_ALGORITHMS for keys
clusterless_algorithm_params (dict) – Parameters for the clusterless algorithms.

Methods

`convert_2D_to_1D_results`(results2D, ...)	Projects a 2D position decoding result to a 1D decoding result.
`copy`()	Makes a copy of the classifier
`estimate_parameters`(fit_args, predict_args)	Estimate the intial conditions and/or discrete transition matrix of the model.
`fit`(position, multiunits[, is_training, ...])	Fit the spatial grid, initial conditions, place field model, and transition matrices.
`fit_continuous_state_transition`([...])	Constructs the transition matrices for the continuous states.
`fit_discrete_state_transition`()	Constructs the transition matrix for the discrete states.
`fit_environments`(position[, environment_labels])	Fits the Environment class on the position data to get information about the spatial environment.
`fit_initial_conditions`()	Constructs the initial probability for the state and each spatial bin.
`fit_multiunits`(position, multiunits[, ...])	Fit the clusterless place field model.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`load_model`([filename])	Load the classifier from a file.
`plot_discrete_state_transition`([...])	Plot heatmap of discrete transition matrix.
`predict`(multiunits[, time, ...])	Predict the probability of spatial position and category from the multiunit spikes and waveforms.
`predict_proba`(results)	Predicts the probability of each state.
`project_1D_position_to_2D`(results[, ...])	Project the 1D most probable position into the 2D track graph space.
`save_model`([filename])	Save the classifier to a pickled file.
`set_fit_request`(*[, encoding_group_labels, ...])	Request metadata passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.
`set_predict_request`(*[, is_compute_acausal, ...])	Request metadata passed to the `predict` method.

Methods

`convert_2D_to_1D_results`	Projects a 2D position decoding result to a 1D decoding result.
`copy`	Makes a copy of the classifier
`estimate_parameters`	Estimate the intial conditions and/or discrete transition matrix of the model.
`fit`	Fit the spatial grid, initial conditions, place field model, and transition matrices.
`fit_continuous_state_transition`	Constructs the transition matrices for the continuous states.
`fit_discrete_state_transition`	Constructs the transition matrix for the discrete states.
`fit_environments`	Fits the Environment class on the position data to get information about the spatial environment.
`fit_initial_conditions`	Constructs the initial probability for the state and each spatial bin.
`fit_multiunits`	Fit the clusterless place field model.
`get_metadata_routing`	Get metadata routing of this object.
`get_params`	Get parameters for this estimator.
`load_model`	Load the classifier from a file.
`plot_discrete_state_transition`	Plot heatmap of discrete transition matrix.
`predict`	Predict the probability of spatial position and category from the multiunit spikes and waveforms.
`predict_proba`	Predicts the probability of each state.
`project_1D_position_to_2D`	Project the 1D most probable position into the 2D track graph space.
`save_model`	Save the classifier to a pickled file.
`set_fit_request`	Request metadata passed to the `fit` method.
`set_params`	Set the parameters of this estimator.
`set_predict_request`	Request metadata passed to the `predict` method.

static convert_2D_to_1D_results(results2D: Dataset, environment2D: Environment, environment1D: Environment) → Dataset#

Projects a 2D position decoding result to a 1D decoding result.

Parameters:

results (xarray.core.dataset.Dataset)
environment2D (replay_trajectory_classification.environments.Environment)
environment1D (replay_trajectory_classification.environments.Environment)

Returns:

results1D

Return type:

xarray.core.dataset.Dataset

Examples

results = classifier.predict(spikes) environment1D = (

Environment(track_graph=track_graph,
place_bin_size=2.0, edge_order=edge_order, edge_spacing=edge_spacing)

.fit_place_grid())

results1D = convert_2D_to_1D_results(: results, classifier.environments[0], environment1D)

copy()#: Makes a copy of the classifier

estimate_parameters(fit_args: dict, predict_args: dict, tolerance: float = 0.0001, max_iter: int = 10, verbose: bool = True, store_likelihood: bool = True, estimate_initial_conditions: bool = True, estimate_discrete_transition: bool = True) → tuple[Dataset, list[float]]#

Estimate the intial conditions and/or discrete transition matrix of the model.

Parameters:

fit_args (dict) – Arguments that would be passed to the fit method.
predict_args (dict) – Arguments that would be passed to the predict method.
tolerance (float, optional) – Smallest change in data log likelihood for there to be no change in likelihood, by default 1e-4
max_iter (int, optional) – Maximum number of iterations, by default 10
verbose (bool, optional) – Log results of each iteration, by default True
store_likelihood (bool, optional) – If True, don’t reestimate the likelihood, by default True
estimate_initial_conditions (bool, optional) – If True, estimate the initial conditions, by default True
estimate_discrete_transition (bool, optional) – If True, estimate the discrete state transition, by default True

Returns:

results (xr.Dataset)
data_log_likelihoods (list, len (n_iter,)) – The data log likelihood of each iteration

fit(position: ndarray, multiunits: ndarray, is_training: ndarray | None = None, encoding_group_labels: ndarray | None = None, environment_labels: ndarray | None = None)[source]#

Fit the spatial grid, initial conditions, place field model, and transition matrices.

Parameters:

position (np.ndarray, shape (n_time, n_position_dims)) – Position of the animal.
multiunits (array_like, shape (n_time, n_marks, n_electrodes)) – Array where spikes are indicated by non-Nan values that correspond to the waveform features for each electrode.
is_training (None or np.ndarray, shape (n_time), optional) – Boolean array to indicate which data should be included in fitting of place fields, by default None
encoding_group_labels (None or np.ndarray, shape (n_time,)) – Label for the corresponding encoding group for each time point
environment_labels (None or np.ndarray, shape (n_time,)) – Label for the corresponding environment for each time point

Return type:

self

fit_continuous_state_transition(continuous_transition_types: list[list[EmpiricalMovement | RandomWalk | RandomWalkDirection1 | RandomWalkDirection2 | Uniform]] = [[RandomWalk(environment_name='', movement_var=6.0, movement_mean=0.0, use_diffusion=False), Uniform(environment_name='', environment2_name=None)], [Uniform(environment_name='', environment2_name=None), Uniform(environment_name='', environment2_name=None)]], position: ndarray | None = None, is_training: ndarray | None = None, encoding_group_labels: ndarray | None = None, environment_labels: ndarray | None = None) → None#

Constructs the transition matrices for the continuous states.

Parameters:

continuous_transition_types (list of list of transition matrix instances, optional) – Types of transition models, by default _DEFAULT_CONTINUOUS_TRANSITIONS
position (np.ndarray, optional) – Position of the animal in the environment, by default None
is_training (np.ndarray, optional) – Boolean array that determines what data to train the place fields on, by default None
encoding_group_labels (np.ndarray, shape (n_time,), optional) – If place fields should correspond to each state, label each time point with the group name For example, Some points could correspond to inbound trajectories and some outbound, by default None
environment_labels (np.ndarray, shape (n_time,), optional) – If there are multiple environments, label each time point with the environment name, by default None

fit_discrete_state_transition()#: Constructs the transition matrix for the discrete states.

fit_environments(position: ndarray, environment_labels: ndarray | None = None) → None#

Fits the Environment class on the position data to get information about the spatial environment.

Parameters:

position (np.ndarray, shape (n_time, n_position_dims))
environment_labels (np.ndarray, optional, shape (n_time,)) – Labels for each time points about which environment it corresponds to, by default None

fit_initial_conditions()#: Constructs the initial probability for the state and each spatial bin.

fit_multiunits(position: ndarray, multiunits: ndarray, is_training: ndarray | None = None, encoding_group_labels: ndarray | None = None, environment_labels: ndarray | None = None)[source]#

Fit the clusterless place field model.

Parameters:

position (np.ndarray, shape (n_time, n_position_dims)) – Position of the animal.
multiunits (array_like, shape (n_time, n_marks, n_electrodes)) – Array where spikes are indicated by non-Nan values that correspond to the waveform features for each electrode.
is_training (None or np.ndarray, shape (n_time), optional) – Boolean array to indicate which data should be included in fitting of place fields, by default None
encoding_group_labels (None or np.ndarray, shape (n_time,)) – Label for the corresponding encoding group for each time point
environment_labels (None or np.ndarray, shape (n_time,)) – Label for the corresponding environment for each time point

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:: routing – A MetadataRequest encapsulating routing information.
Return type:: MetadataRequest

get_params(deep=True)#

Get parameters for this estimator.

Parameters:: deep (bool, default=True) – 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:: dict

static load_model(filename: str = 'model.pkl')#

Load the classifier from a file.

Parameters:: filename (str, optional)
Return type:: classifier instance

plot_discrete_state_transition(state_names: list[str] | None = None, cmap: str = 'Oranges', ax: Axes | None = None, convert_to_seconds: bool = False, sampling_frequency: int = 1) → None#

Plot heatmap of discrete transition matrix.

Parameters:

state_names (list[str], optional) – Names corresponding to each discrete state, by default None
cmap (str, optional) – matplotlib colormap, by default “Oranges”
ax (matplotlib.axes.Axes, optional) – Plotting axis, by default plots to current axis
convert_to_seconds (bool, optional) – Convert the probabilities of state to expected duration of state, by default False
sampling_frequency (int, optional) – Number of samples per second, by default 1

predict(multiunits: ndarray, time: ndarray | None = None, is_compute_acausal: bool = True, use_gpu: bool = False, state_names: list[str] | None = None, store_likelihood: bool = False) → Dataset[source]#

Predict the probability of spatial position and category from the multiunit spikes and waveforms.

Parameters:

multiunits (array_like, shape (n_time, n_marks, n_electrodes)) – Array where spikes are indicated by non-Nan values that correspond to the waveform features for each electrode.
time (np.ndarray or None, shape (n_time,), optional) – Label the time axis with these values.
is_compute_acausal (bool, optional) – If True, compute the acausal posterior.
use_gpu (bool, optional) – Use GPU for the state space part of the model, not the likelihood.
state_names (None or array_like, shape (n_states,)) – Label the discrete states.
store_likelihood (bool, optional) – Store the likelihood to reuse in next computation.

Returns:

results

Return type:

xarray.Dataset

static predict_proba(results: Dataset) → Dataset#

Predicts the probability of each state.

Parameters:: results (xr.Dataset)
Returns:: results
Return type:: xr.Dataset

project_1D_position_to_2D(results: Dataset, posterior_type='acausal_posterior') → ndarray#

Project the 1D most probable position into the 2D track graph space.

Only works for single environment.

Parameters:

results (xr.Dataset)
posterior_type (causal_posterior | acausal_posterior | likelihood)

Returns:

map_position2D

Return type:

np.ndarray

save_model(filename: str = 'model.pkl') → None#

Save the classifier to a pickled file.

Parameters:: filename (str, optional)

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

encoding_group_labels (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for encoding_group_labels parameter in fit.
environment_labels (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for environment_labels parameter in fit.
is_training (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for is_training parameter in fit.
multiunits (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for multiunits parameter in fit.
position (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for position parameter in fit.

Returns:

self – The updated object.

Return type:

object

set_params(**params)#

Set the parameters of this estimator.

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

Parameters:: **params (dict) – Estimator parameters.
Returns:: self – Estimator instance.
Return type:: estimator instance

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to predict.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

is_compute_acausal (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for is_compute_acausal parameter in predict.
multiunits (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for multiunits parameter in predict.
state_names (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for state_names parameter in predict.
store_likelihood (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for store_likelihood parameter in predict.
time (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for time parameter in predict.
use_gpu (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for use_gpu parameter in predict.

Returns:

self – The updated object.

Return type:

object

replay_trajectory_classification.classifier.ClusterlessClassifier#

This Page