Translating API from 0.x to 1.x

[yuvalwas]

Hello, I am somewhat lost trying to translate between the two interfaces. My data is from a linear track and I want to have the classifier represent the two directions separately to account for remapping between directions. If I understand correctly, in 0.x I would do something like this:

clusterless_algorithm = 'multiunit_likelihood'
clusterless_algorithm_params = {
    model_kwargs': {
        bandwidth': np.array([mark_std, mark_std, mark_std, mark_std, pos_std]) 
    }
}

continuous_transition_types = [
    ['random_walk', 'uniform',  'uniform',   'uniform'],
    ['uniform',            'uniform',  'uniform',   'uniform''],
    ['uniform',            'uniform',  'random_walk', 'uniform'],
    ['uniform',            'uniform',  'uniform',   'uniform'],
    ]

encoding_group_to_state = [-1, -1, -1, 1, 1, 1] # corresponds to direction

classifier = ClusterlessClassifier(movement_var=movement_var,
                                   place_bin_size=pos_bin_size,
                                   clusterless_algorithm=clusterless_algorithm,
                                   clusterless_algorithm_params=clusterless_algorithm_params,
                                   continuous_transition_types=continuous_transition_types,
                                   discrete_transition_diag=state_diag_val)

classifier.fit(position,
               multiunits,
               encoding_group_labels=direction,
               encoding_group_to_state=encoding_group_to_state,
               is_training=is_training)

My translation attempt to 1.x is:

clusterless_algorithm = 'multiunit_likelihood'
clusterless_algorithm_params = {
          'mark_std' : mark_std, 
          'position_std' : pos_std
}

continuous_transition_types = [
    [RandomWalk(movement_var=movement_var), Uniform(),   Uniform(),                             Uniform()],
    [Uniform(),                             Uniform(),   Uniform(),                             Uniform()],
    [Uniform(),                             Uniform(),   RandomWalk(movement_var=movement_var), Uniform()],
    [Uniform(),                             Uniform(),   Uniform(),                             Uniform()],
    ]

encoding_group_to_state = [-1, -1, 1, 1] # corresponds to direction

classifier = ClusterlessClassifier(environment=Environment(place_bin_size=pos_bin_size),
                                   continuous_transition_types=continuous_transition_types,
                                   clusterless_algorithm=clusterless_algorithm,
                                   clusterless_algorithm_params=clusterless_algorithm_params,
                                   discrete_transition_diag=DiagonalDiscrete(state_diag_val))

classifier.fit(position,
               multiunits,
               encoding_group_labels=direction,
               encoding_group_to_state=encoding_group_to_state,
               is_training=is_training)

Problems:

1. encoding_group_to_state is no longer a keyword of ClusterlessClassifier.fit and I don't know what do to with it.
2. I don't understand from the docs the distinction between different encoding groups and different environments, which of them I should use, and how.

Thank you

[edeno]

Hi @yuvalwas ,

I believe this notebook should help: https://github.com/Eden-Kramer-Lab/replay_trajectory_classification/blob/master/notebooks/work_in_progress/Test_Inbound_Outbound.ipynb

Basically the same model but using the ObservationModel to define the groups and the encoding_group_labels to replace encoding_group_to_state