biopython

v1.71.0

biopython v1.71.0 Bio.HMM.Trainer.BaumWelchTrainer

Trainer that uses the Baum-Welch algorithm to estimate parameters.

These should be used when a training sequence for an HMM has unknown paths for the actual states, and you need to make an estimation of the model parameters from the observed emissions.

This uses the Baum-Welch algorithm, first described in Baum, L.E. 1972. Inequalities. 3:1-8 This is based on the description in ‘Biological Sequence Analysis’ by Durbin et al. in section 3.3

This algorithm is guaranteed to converge to a local maximum, but not necessarily to the global maxima, so use with care!

Link to this section Summary

Functions

__init__()

Initialize the trainer

train()

Estimate the parameters using training sequences

update_emissions()

Add the contribution of a new training sequence to the emissions

update_transitions()

Add the contribution of a new training sequence to the transitions

Link to this section Functions

Link to this function __init__()

Initialize the trainer.

Arguments:

  • markov_model - The model we are going to estimate parameters for. This should have the parameters with some initial estimates, that we can build from.
Link to this function train()

Estimate the parameters using training sequences.

The algorithm for this is taken from Durbin et al. p64, so this is a good place to go for a reference on what is going on.

Arguments:

  • training_seqs — A list of TrainingSequence objects to be used for estimating the parameters.
  • stopping_criteria — A function, that when passed the change in log likelihood and threshold, will indicate if we should stop the estimation iterations.
  • dp_method — A class instance specifying the dynamic programming implementation we should use to calculate the forward and backward variables. By default, we use the scaling method.
Link to this function update_emissions()

Add the contribution of a new training sequence to the emissions.

Arguments:

  • emission_counts — A dictionary of the current counts for the emissions
  • training_seq — The training sequence we are working with
  • forward_vars — Probabilities calculated using the forward algorithm.
  • backward_vars — Probabilities calculated using the backwards algorithm.
  • training_seq_prob - The probability of the current sequence.

This calculates E_{k}(b) (the estimated emission probability for emission letter b from state k) using formula 3.21 in Durbin et al.

Link to this function update_transitions()

Add the contribution of a new training sequence to the transitions.

Arguments:

  • transition_counts — A dictionary of the current counts for the transitions
  • training_seq — The training sequence we are working with
  • forward_vars — Probabilities calculated using the forward algorithm.
  • backward_vars — Probabilities calculated using the backwards algorithm.
  • training_seq_prob - The probability of the current sequence.

This calculates A_{kl} (the estimated transition counts from state k to state l) using formula 3.20 in Durbin et al.