Hidden Markov Models in Python, with scikit-learn like API

I am trying to create audio vocabulary from MFCC features by applying HMM. Since I have 10 speakers in the MFCC features. I need 50 states per speaker. So I used N = 500 states and it throws Memory error, but it works fine with N =100 states.

Memory Error is because of computational in efficiency of a machine or due to in proper initialization?

Here is my code

import numpy as np
from hmmlearn import hmm
import librosa
import matplotlib.pyplot as plt

def getMFCC(episode):

    filename = getPathToGroundtruth(episode)

    y, sr = librosa.load(filename)  # Y gives 

    data = librosa.feature.mfcc(y=y, sr=sr)

    return data

def hmm_init(n,data):  #n = states d = no of feautures

    states =[]

    model = hmm.GaussianHMM(n_components=N, covariance_type="full")

    model.transmat_ = np.ones((N, N)) / N

    model.startprob_ = np.ones(N) / N

    fit = model.fit(data.T)

    z=fit.decode(data.T,algorithm='viterbi')[1]

    states.append(z)

    return states

data_m = getMFCC(1)  # Provides MFCC features of numpy array [20 X 56829]

N = 500

D= len(data)

states = hmm_init(N,data)

In [23]: run Final_hmm.py
---------------------------------------------------------------------------
MemoryError                               Traceback (most recent call last)
/home/elancheliyan/Final_hmm.py in <module>()
     73 D= len(data)
     74 
---> 75 states = hmm_init(N,data)
     76 states.dump("states")
     77 

/home/elancheliyan/Final_hmm.py in hmm_init(n, data)
     57     model.startprob_ = np.ones(N) / N
     58 
---> 59     fit = model.fit(data.T)
     60 
     61     z=fit.decode(data.T,algorithm='viterbi')[1]

/cal/homes/elancheliyan/.local/lib/python3.5/site-packages/hmmlearn-0.2.1-py3.5-linux-x86_64.egg/hmmlearn/base.py in fit(self, X, lengths)
    434                 self._accumulate_sufficient_statistics(
    435                     stats, X[i:j], framelogprob, posteriors, fwdlattice,
--> 436                     bwdlattice)
    437 
    438             # XXX must be before convergence check, because otherwise

/cal/homes/elancheliyan/.local/lib/python3.5/site-packages/hmmlearn-0.2.1-py3.5-linux-x86_64.egg/hmmlearn/hmm.py in _accumulate_sufficient_statistics(self, stats, obs, framelogprob, posteriors, fwdlattice, bwdlattice)
    221                                           posteriors, fwdlattice, bwdlattice):
    222         super(GaussianHMM, self)._accumulate_sufficient_statistics(
--> 223             stats, obs, framelogprob, posteriors, fwdlattice, bwdlattice)
    224 
    225         if 'm' in self.params or 'c' in self.params:

/cal/homes/elancheliyan/.local/lib/python3.5/site-packages/hmmlearn-0.2.1-py3.5-linux-x86_64.egg/hmmlearn/base.py in _accumulate_sufficient_statistics(self, stats, X, framelogprob, posteriors, fwdlattice, bwdlattice)
    620                 return
    621 
--> 622             lneta = np.zeros((n_samples - 1, n_components, n_components))
    623             _hmmc._compute_lneta(n_samples, n_components, fwdlattice,
    624                                  log_mask_zero(self.transmat_),

MemoryError:

In sklearn GMM was replaced by GaussianMixture. See https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/mixture/gmm.py:

class GMM(_GMMBase): """ Legacy Gaussian Mixture Model .. deprecated:: 0.18 This class will be removed in 0.20. Use :class:sklearn.mixture.GaussianMixture instead. """

However, hmmlearn still uses the old version. A pull request is needed to upgrade hmmlearn to work with the newer API.

Hi, today I learned about your package, started to use it, faced the memory problem, and came up with a PR that fixes it.

I've exploited the lengths option and added another meaning to it. Currently, for the GMMHMM only. Curious users will find a way to extend my implementation to other models as well.

This also partially addresses the comment left in https://github.com/hmmlearn/hmmlearn/commit/08dee6640483cda232f7d2fcc7935d4008f4d368:

https://github.com/hmmlearn/hmmlearn/blob/0562ca65756ffb60da836eeeb1845e61767c705b/lib/hmmlearn/hmm.py#L918-L922

I got rid of the unnecessary 'centered' arrays in the stats dict. If you don't want to store the post_comp_mix matrices in the stats, the logic of computing intermediate variables - c_n and c_d for the covariance - should be moved from the _do_mstep to _accumulate_sufficient_statistics function. Since this is my first PR, I've decided not to rummage through your code a lot. In either case, this should be considered in a separate PR, if you will.

Best, Danylo

Hi,

I used the hmm module from sklearn and tried to replace it by the hmmlearn module. Unfortunately I could not import it to my notebook.

from hmmlearn import hmm --------------------------------------------------------------------------- ImportError Traceback (most recent call last) <ipython-input-7-8b8c029fb053> in <module>() ----> 1 from hmmlearn import hmm

ImportError: cannot import name hmm

I tried first pip-3.3 install git+https://github.com/hmmlearn/hmmlearn.git

As this didn't work I cloned the project and run the setup.py (with python 3.3) but I still get an import error.

If I try to import

import hmmlearn.hmm

I get another error

ImportError Traceback (most recent call last) <ipython-input-8-8dbb2cfe75b2> in <module>() ----> 1 import hmmlearn.hmm

/home/ipython/python/lib/python3.3/site-packages/hmmlearn/hmm.py in <module>() 22 from sklearn import cluster 23 ---> 24 from .utils.fixes import log_multivariate_normal_density 25 26 from . import _hmmc

ImportError: No module named 'hmmlearn.utils'

What did I do wrong?

Cheers, Evelyn

When I used GaussianHMM().fit() to train HMM, there is a RuntimeWarning: divide by zero encountered in log. Then I found that the start probability would approach to 0 after several EM iterations. My question is how to avoid probability approaching to 0 ?

I get hmmlearn/_hmmc.c:239:28: fatal error: numpy/npy_math.h: No such file or directory yet the installation seems to finish successfully.

requirements.txt file:

click==6.7
cython==0.25.2
joblib==0.11
numpy==1.12.1
pandas==0.19.2
python-speech-features==0.5
scikit-learn==0.18.1
scipy==0.19.0
hmmlearn==0.2.0

Running setup.py bdist_wheel for hmmlearn: started
  Running setup.py bdist_wheel for hmmlearn: finished with status 'error'
  Complete output from command /opt/conda/bin/python -u -c "import setuptools, tokenize;__file__='/tmp/pip-build-8l6nu2n1/hmmlearn/setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, __file__, 'exec'))" bdist_wheel -d /tmp/tmpi_45qjtvpip-wheel- --python-tag cp36:
  running bdist_wheel
  running build
  running build_py
  creating build
  creating build/lib.linux-x86_64-3.6
  creating build/lib.linux-x86_64-3.6/hmmlearn
  copying hmmlearn/hmm.py -> build/lib.linux-x86_64-3.6/hmmlearn
  copying hmmlearn/utils.py -> build/lib.linux-x86_64-3.6/hmmlearn
  copying hmmlearn/base.py -> build/lib.linux-x86_64-3.6/hmmlearn
  copying hmmlearn/__init__.py -> build/lib.linux-x86_64-3.6/hmmlearn
  creating build/lib.linux-x86_64-3.6/hmmlearn/tests
  copying hmmlearn/tests/test_utils.py -> build/lib.linux-x86_64-3.6/hmmlearn/tests
  copying hmmlearn/tests/test_gaussian_hmm.py -> build/lib.linux-x86_64-3.6/hmmlearn/tests
  copying hmmlearn/tests/test_gmm_hmm.py -> build/lib.linux-x86_64-3.6/hmmlearn/tests
  copying hmmlearn/tests/test_multinomial_hmm.py -> build/lib.linux-x86_64-3.6/hmmlearn/tests
  copying hmmlearn/tests/test_base.py -> build/lib.linux-x86_64-3.6/hmmlearn/tests
  copying hmmlearn/tests/__init__.py -> build/lib.linux-x86_64-3.6/hmmlearn/tests
  running build_ext
  building 'hmmlearn._hmmc' extension
  creating build/temp.linux-x86_64-3.6
  creating build/temp.linux-x86_64-3.6/hmmlearn
  gcc -pthread -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/conda/include/python3.6m -c hmmlearn/_hmmc.c -o build/temp.linux-x86_64-3.6/hmmlearn/_hmmc.o -O3
  hmmlearn/_hmmc.c:239:28: fatal error: numpy/npy_math.h: No such file or directory
   #include "numpy/npy_math.h"
                              ^
  compilation terminated.
  error: command 'gcc' failed with exit status 1
  
  ----------------------------------------
  Failed building wheel for hmmlearn
  Running setup.py clean for hmmlearn
Successfully built python-speech-features
Failed to build hmmlearn
Installing collected packages: click, cython, joblib, numpy, pytz, python-dateutil, pandas, python-speech-features, scikit-learn, scipy, hmmlearn
  Running setup.py install for hmmlearn: started
    Running setup.py install for hmmlearn: finished with status 'done'
**Successfully installed** click-6.7 cython-0.25.2 **hmmlearn-0.2.0** joblib-0.11 numpy-1.12.1 pandas-0.19.2 python-dateutil-2.6.0 python-speech-features-0.5 pytz-2017.2 scikit-learn-0.18.1 scipy-0.19.0

my OS is win7 x64 . visual studio 2015, also visual studio 2013, and python 3.5 x64(by anaconda) are set up. hmmlearn is set up successfully. and validated by the code: >>>import hmmlearn >>> hmmlearn.version and the output is '0.2.0' which is last version of hmmlearn. but, if i put the code like the following, >>>from hmmlearn import hmm i get the error as the following,

C:\Anaconda3_64\python.exe E:/pycharm/plot_hmm_stock_analysis/hmm_stock_analysis.py Traceback (most recent call last): File "E:/pycharm/plot_hmm_stock_analysis/hmm_stock_analysis.py", line 17, in from hmmlearn import hmm File "C:\Anaconda3_64\lib\site-packages\hmmlearn-0.2.0-py3.5-win-amd64.egg\hmmlearn\hmm.py", line 14, in from sklearn import cluster File "C:\Anaconda3_64\lib\site-packages\sklearn__init__.py", line 57, in from .base import clone File "C:\Anaconda3_64\lib\site-packages\sklearn\base.py", line 11, in from .utils.fixes import signature File "C:\Anaconda3_64\lib\site-packages\sklearn\utils__init__.py", line 11, in from .validation import (as_float_array, File "C:\Anaconda3_64\lib\site-packages\sklearn\utils\validation.py", line 16, in from ..utils.fixes import signature File "C:\Anaconda3_64\lib\site-packages\sklearn\utils\fixes.py", line 324, in from scipy.sparse.linalg import lsqr as sparse_lsqr File "C:\Anaconda3_64\lib\site-packages\scipy\sparse\linalg__init__.py", line 109, in from .isolve import * File "C:\Anaconda3_64\lib\site-packages\scipy\sparse\linalg\isolve__init__.py", line 6, in from .iterative import * File "C:\Anaconda3_64\lib\site-packages\scipy\sparse\linalg\isolve\iterative.py", line 7, in from . import _iterative ImportError: DLL load failed: The specified module could not be found.

why? and how to fix it!?

by the way, if in cmd, using "pip freeze" commond, it shows hmmlearn and the version of it is 0.2.0. BUT, if using "conda list", no hmmlearn shows!!

Hi all, I am having a problem when trying to fit multiple GMMHMM models to solve a classification problem of emotions recognition from speech samples. Basically, the models often don't converge: even if the monitor reports 'True' if printed, I can see in the history that the likelihood is not strictly increasing. Actually, it decreases at some point and the training procedure stops.

Here, I report only the procedure for training one of the models (I should have seven, each one trained with a different training set). The data loaded are attached: data_training.npy.zip

from hmmlearn import hmm
import numpy as np 
data = np.load('data_training.npy', allow_pickle=True)


hmm = hmm.GMMHMM(n_components=2, n_mix=2,n_iter=1000, covariance_type="diag", verbose=True ) 

X_sequence_concat = np.concatenate(data) 
lengths = []
for el in data:
    lengths.append(len(el))

hmm.fit(X_sequence_concat, np.array(lengths))   
print("Is the HMM training converged? " + str(hmm.monitor_.converged))

In my actual implementation I have to do this for seven different models and sometimes I get this problem and sometimes I don't, as you can see from the results reported below:

Can you please help me? I'm really struggling with this and I can't find a possible cause of the problem.

Thanks in advance!

Hi!

I am trying to install hmmlearn as described in the markdown document but I am running into problems. Specifically, I get the following error:

$ python setup.py install
running install
running bdist_egg
running egg_info
writing hmmlearn.egg-info/PKG-INFO
writing top-level names to hmmlearn.egg-info/top_level.txt
writing dependency_links to hmmlearn.egg-info/dependency_links.txt
reading manifest file 'hmmlearn.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
writing manifest file 'hmmlearn.egg-info/SOURCES.txt'
installing library code to build/bdist.macosx-10.10-x86_64/egg
running install_lib
running build_py
running build_ext
building 'hmmlearn._hmmc' extension
clang -fno-strict-aliasing -fno-common -dynamic -g -O2 -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -I/usr/local/include -I/usr/local/opt/openssl/include -I/usr/local/opt/sqlite/include -I/usr/local/Cellar/python/2.7.10_2/Frameworks/Python.framework/Versions/2.7/include/python2.7 -c hmmlearn/_hmmc.c -o build/temp.macosx-10.10-x86_64-2.7/hmmlearn/_hmmc.o -O3
hmmlearn/_hmmc.c:239:10: fatal error: 'numpy/arrayobject.h' file not found
#include "numpy/arrayobject.h"
         ^
1 error generated.
error: command 'clang' failed with exit status 

I have numpy, scipy and scikit-learn installed on my system and they work fine when I use them in my other scripts. Can you help me install the package?

I want to export the trans matrix, and emission matrix from hmmlearn , and used as model parameters in forward algorithm written by c++, it'clear that "transmat_" attribute is the trans matrix of hiden state ,while how to get the so called emission matrix ? does "means_" attribute of model represent the emission matrix ? thanks !

GMMHMM has parameters stratprob_ and transmatprob_ corresponding to π and A,but how do I print the B in GMMHMM

In rare cases, directly instantiating BaseHMM can be useful for end users, e.g. to sample states from a Markov chain. So make the class public.

Thoughts?

Since most of the cython code releases the GIL, the joblib threading backend can be used to parallelize some of the operations. Since a Python dictionary is used after the calculations to collect statistics, I used a threading lock to prevent races.

I am not 100% sure if I missed any other places which would need to be locked however.

This PR depends on this one https://github.com/hmmlearn/hmmlearn/pull/439

Added score_batches method which returns a list of scores (one for each sequence) instead of a cumulative score.

See https://github.com/hmmlearn/hmmlearn/issues/272

If a random-state is specified, pass it through to the models. Useful for reproducible benchmarks. Also, require that the minimum number of iterations are reached during training.

MultinomialHMM class is written in hmm.py, but tests are not finished/just have stubs for now.

Renamed Multinomial HMM functions and test file to Categorical HMM, addressed #335 and #340

We have set startprob_prior, transmat_prior, so why not initialize startprob_, transmat_ with Dirichlet distribution, just callingnumpy.random.dirichlet?

I am reporting a bug

I have found that when I set the GaussianHMM with the following parameters, the accuracy of my model is different than when I use the default parameters. There is quite a large difference in accuracy with the default parameters achieving 0.82, and the model when I input the same parameters only achieves 0.18. I cannot share my data unfortunately. :(

With default parameters: gaus_waveHMM_d = hmm_.GaussianHMM(n_components=2, algorithm='viterbi', verbose=True)

Which outputs: GaussianHMM(algorithm='viterbi', covariance_type='diag', covars_prior=0.01, covars_weight=1, init_params='stmc', means_prior=0, means_weight=0, min_covar=0.001, n_components=2, n_iter=10, params='stmc', random_state=None, startprob_prior=1.0, tol=0.01, transmat_prior=1.0, verbose=True)

When I input the parameters: gausHMM_diag = hmm_.GaussianHMM(n_components=2, covariance_type='diag', algorithm='viterbi', verbose=True, n_iter=10, tol=0.01, min_covar=0.001) Which outputs: GaussianHMM(algorithm='viterbi', covariance_type='diag', covars_prior=0.01, covars_weight=1, init_params='stmc', means_prior=0, means_weight=0, min_covar=0.001, n_components=2, n_iter=10, params='stmc', random_state=None, startprob_prior=1.0, tol=0.01, transmat_prior=1.0, verbose=True)

I tried to trace through the code, but I can't pin point why this is changing. Is there a reason for this discrepancy?

Thanks for any help/explanation!

Hello, super-appreciative about the creation of this package!!

A question: is it possible, for the GMMHMM case, to extract the probability of each mixture component when reconstructing a time series?

i.e., right now, log_probs tells us, given an HMM, and a sample sequence, what the probability of being in each state is, at each point in the sequence.

But the model also includes a step where, once you're in a particular state, you sample from one of the mixture components with some probability. In an ordinary GMM, we can also figure out the probability that a particular observation was drawn from each of the mixture components.

It is possible to extract this? Presumably, you would have, for each point in the sequence, an n_components x n_mix matrix. This would tell you the joint probability that you drew from, say Hidden State 1, Mixture Component 1.

This may be hidden somewhere in the code itself; I think EM/Viterbi will estimate this as part of the reconstruction anyway...