mchmm

A Python package for statistical modeling with Markov chains and Hidden Markov models. Built on NumPy and SciPy, mchmm provides efficient implementations of core algorithms including Viterbi decoding and Baum-Welch parameter estimation. The package also includes visualization capabilities for understanding model structure and behavior.

Key Features

• Discrete Markov Chains: Build transition models from sequence data with automatic state inference
• Hidden Markov Models: Implement HMMs with customizable observation and state spaces
• Viterbi Algorithm: Find most likely state sequences for new observations
• Baum-Welch Algorithm: Learn HMM parameters from unlabeled sequence data
• Statistical Testing: Built-in chi-squared tests for model validation
• Visualization: Generate directed graphs of Markov models using Graphviz
• Simulation: Generate synthetic sequences from trained models

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If you find this package useful, please consider donating any amount of money. This will help me spend more time on supporting open-source software.

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Installation

Install from PyPI:

pip install mchmm

Or install from source:

git clone https://github.com/maximtrp/mchmm.git
cd mchmm
pip install . --user

Dependencies

• NumPy - Numerical computing
• SciPy - Scientific computing and statistics
• Graphviz - Graph visualization (optional)

Quick Start

Discrete Markov Chains

Initializing a Markov chain using some data.

import mchmm as mc
a = mc.MarkovChain().from_data('AABCABCBAAAACBCBACBABCABCBACBACBABABCBACBBCBBCBCBCBACBABABCBCBAAACABABCBBCBCBCBCBCBAABCBBCBCBCCCBABCBCBBABCBABCABCCABABCBABC')

Now, we can look at the observed transition frequency matrix:

a.observed_matrix
# array([[ 7., 18.,  7.],
#        [19.,  5., 29.],
#        [ 5., 30.,  3.]])

And the observed transition probability matrix:

a.observed_p_matrix
# array([[0.21875   , 0.5625    , 0.21875   ],
#        [0.35849057, 0.09433962, 0.54716981],
#        [0.13157895, 0.78947368, 0.07894737]])

You can visualize your Markov chain. First, build a directed graph with graph_make() method of MarkovChain object. Then render() it.

graph = a.graph_make(
    format="png",
    graph_attr=[("rankdir", "LR")],
    node_attr=[("fontname", "Roboto bold"), ("fontsize", "20")],
    edge_attr=[("fontname", "Iosevka"), ("fontsize", "12")]
)
graph.render()

Here is the result:

Pandas can help us annotate columns and rows:

import pandas as pd
pd.DataFrame(a.observed_matrix, index=a.states, columns=a.states, dtype=int)
#      A   B   C
#  A   7  18   7
#  B  19   5  29
#  C   5  30   3

Viewing the expected transition frequency matrix:

a.expected_matrix
# array([[ 8.06504065, 13.78861789, 10.14634146],
#        [13.35772358, 22.83739837, 16.80487805],
#        [ 9.57723577, 16.37398374, 12.04878049]])

Calculating Nth order transition probability matrix:

a.n_order_matrix(a.observed_p_matrix, order=2)
# array([[0.2782854 , 0.34881028, 0.37290432],
#        [0.1842357 , 0.64252707, 0.17323722],
#        [0.32218957, 0.21081868, 0.46699175]])

Carrying out a chi-squared test:

a.chisquare(a.observed_matrix, a.expected_matrix, axis=None)
# Power_divergenceResult(statistic=47.89038802624337, pvalue=1.0367838347591701e-07)

Finally, let's simulate a Markov chain given our data.

ids, states = a.simulate(10, start='A', seed=np.random.randint(0, 10, 10))
ids
# array([0, 2, 1, 0, 2, 1, 0, 2, 1, 0])

states
# array(['A', 'C', 'B', 'A', 'C', 'B', 'A', 'C', 'B', 'A'], dtype='<U1')

"".join(states)
# 'ACBACBACBA'

Hidden Markov Models

Build HMMs from paired observation and state sequences. This example uses a DNA fragment with TATA box annotation:

import mchmm as mc
obs_seq = 'AGACTGCATATATAAGGGGCAGGCTG'
sts_seq = '00000000111111100000000000'
a = mc.HiddenMarkovModel().from_seq(obs_seq, sts_seq)

Unique states and observations are automatically inferred:

a.states
# ['0' '1']

a.observations
# ['A' 'C' 'G' 'T']

The transition probability matrix for all states can be accessed using tp attribute:

a.tp
# [[0.94444444 0.05555556]
#  [0.14285714 0.85714286]]

There is also ep attribute for the emission probability matrix for all states and observations.

a.ep
# [[0.21052632 0.21052632 0.47368421 0.10526316]
#  [0.57142857 0.         0.         0.42857143]]

Converting the emission matrix to Pandas DataFrame:

import pandas as pd
pd.DataFrame(a.ep, index=a.states, columns=a.observations)
#            A         C         G         T
#  0  0.210526  0.210526  0.473684  0.105263
#  1  0.571429  0.000000  0.000000  0.428571

Directed graph of the hidden Markov model:

Graph can be visualized using graph_make method of HiddenMarkovModel object:

graph = a.graph_make(
    format="png", graph_attr=[("rankdir", "LR"), ("ranksep", "1"), ("rank", "same")]
)
graph.render()

Viterbi Algorithm

Decode the most likely state sequence for new observations:

new_obs = "GGCATTGGGCTATAAGAGGAGCTTG"
vs, vsi = a.viterbi(new_obs)
# states sequence
print("VI", "".join(vs))
# observations
print("NO", new_obs)
# VI 0000000001111100000000000
# NO GGCATTGGGCTATAAGAGGAGCTTG

Baum-Welch Algorithm

Learn HMM parameters from unlabeled sequence data using expectation-maximization:

obs_seq = 'AGACTGCATATATAAGGGGCAGGCTG'
a = hmm.HiddenMarkovModel().from_baum_welch(obs_seq, states=['0', '1'])
# training log: KL divergence values for all iterations

a.log
# {
#     'tp': [0.008646969455670256, 0.0012397829805491124, 0.0003950986109761759],
#     'ep': [0.09078874423746826, 0.0022734816599056084, 0.0010118204023946836],
#     'pi': [0.009030829793043593, 0.016658391248503462, 0.0038894983546756065]
# }

The inferred transition (tp), emission (ep) probability matrices and initial state distribution (pi) can be accessed as shown:

a.ep, a.tp, a.pi

This model can be decoded using Viterbi algorithm:

new_obs = "GGCATTGGGCTATAAGAGGAGCTTG"
vs, vsi = a.viterbi(new_obs)
print("VI", "".join(vs))
print("NO", new_obs)
# VI 0011100001111100000001100
# NO GGCATTGGGCTATAAGAGGAGCTTG