Behavpy_HMM

About

Behavpy_HMM is an extension of behavpy that revolves around the use of Hidden Markov Models (HMM) to segment and predict sleep and awake stages in drosophila. A good introduction to HMMs is available here: https://web.stanford.edu/~jurafsky/slp3/A.pdf .

The provide a basic overview, HMMS are a generative probabilistic model, in which there is an assumption that a sequence of observable variables is generated by a sequence of internal hidden states. The transition probabilities between hidden states are assumed to follow the principles of a first order Markov chain.

Within this tutorial we will apply this framework to the analysis of sleep in Drosophila, using the observable variable 'movement' to predict the hidden states "deep sleep, light sleep, quiet awake, active awake".

Hmmlearn

BehavpyHMM utilises the brilliant python package Hmmlearn under the hood. Currently behavpy_HMM only uses the categorical version of the models available, as such it can only be trained and decode sequences that are categorical and in an integer form. If you wish to use their other models (multinomial, gaussian and guassian mixture models) or want to read more on their capabilities, head to their docs page for more information: https://hmmlearn.readthedocs.io/en/latest/index.html

Initialising behavpy_HMM

The HMM variant of behavpy is initialised in the same manner as the behavpy class. In fact, it is just a sub-classed version of behavpy so all the previous methods are still applicable, alongside the usual pandas capabilities.

import ethoscopy as etho

# You can load you data using the previously mentioned functions
meta = etho.link_meta_index(meta_loc, remote, local)
data = etho.load_ethoscope(meta, min_time = 24, max_time = 48, reference_hour = 9.0)

# Remember you can save the data and metadata as pickle files, see the downloading section for a reminder about it

# to initialise a behavpy object, just call the class with the data and meta as arguments. Have check = True to ensure the ids match.
df = etho.behavpy_HMM(data, meta, check = True)