| Name | vlmc JSON |
| Version |
0.2.0
JSON |
| download |
| home_page | None |
| Summary | Variable Length Markov Chain |
| upload_time | 2023-06-07 16:09:20 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | None |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# Variable Length Markov Model (VLMC)
[](https://pepy.tech/project/vlmc)
[](https://pypi.org/project/vlmc/)
Implementation of Variable Length Markov Chains (VLMC) for Python.
Suffix tree building is done top-down using the [Peres-Shield](https://link.springer.com/chapter/10.1007/11557067_24) order estimation method.
It is written in Rust with Python Bindings.
##### Contents
- [Installation](#installation)
* [Compiling from source](#compilation-from-source)
- [Usage](#usage)
- [`fit`](#fit)
- [`suffix`](#get_suffix)
- [`counts`](#get_counts)
- [`distribution`](#get_distribution)
- [`contexts`](#get_contexts)
- [Future](#todo)
## Installation
Pre-built packages for many Linux, Windows, and OSX systems are available
in [PyPI](https://pypi.org/project/vlmc/) and can be installed with:
```sh
pip install vlmc
```
On uncommon architectures, you may need to first
[install Cargo](https://doc.rust-lang.org/cargo/getting-started/installation.html) before running `pip install vlmc`.
### Compilation from source
In order to compile from source you will need to [install Rust/Cargo](https://doc.rust-lang.org/cargo/getting-started/installation.html) and [maturin](https://github.com/PyO3/maturin#maturin) for the python bindings.
Maturin is best used within a Python virtual environment:
```sh
# activate your desired virtual environment first, then:
pip install maturin
git clone https://github.com/antonio-leitao/vlmc.git
cd vlmc
# build and install the package:
maturin develop --release
```
# Usage
```python
import vlmc
tree = vlmc.VLMC(max_depth, alphabet_size, n_jobs=-1)
```
Parameters:
- `max_depth`: Maximum depth of tree. Subsequences whose length exceed the `max_depth` will not be considered nor counted.
- `alphabet_size`: Total number of symbols in the alphabet. This number has to be bigger than the highest integer encountered, else it will cause runtime errors.
- `n_jobs`: Number of subprocesses to spawn when running the vlmc. Choose `-1` for using all available processes.
### `fit`
> **Note**
> fit method returns `None` and not `self`. This is by design as to not expose the rust object to python.
```python
data = [
[1,2,3],
[2,3],
[1,0,1],
[2]
]
tree.fit(data)
```
Arguments:
- `data`: List of lists containing sequences of discrete values to fit on. Values are assumed to be integers form `0` to `alphabet_size`. List is expected to be two dimensional.
### `get_suffix`
Given a sequence, returns the longest suffix that is present in the VLMC.
```python
suffix = tree.get_suffix(sequence)
```
Arguments:
- `sequence`: list of integers representing a sequence of discrete varaibles.
Returns:
- `suffix` : longest suffix of sequence that is present in the VLMC.
### `get_counts`
Gets the total number of occurences of a given sequence of integers.
Will throw a `KeyError` if the sequence is not a tree node. Consider using `get_suffix` to make sure to get a tree node.
```python
counts = tree.get_counts(sequence)
```
Arguments:
- `sequence`: list of integers representing a sequence of discrete varaibles.
Returns:
- `counts` : integer
### `get_distribution`
Gets the vector of probabilities over the entire alphabet for the given sequence.
Will throw a `KeyError` if the sequence is not a tree node. Consider using `get_suffix` to make sure to get a tree node.
```python
probabilities = tree.get_distribution(sequence)
```
Arguments:
- `sequence`: list of integers representing a sequence of discrete variables.
Returns:
- `probabilities` : list of floats representing the probability of observing a specific state (index) as the next symbol.
### `get_contexts`
```python
contexts = tree.get_contexts()
```
Returns:
- `contexts`: list of relevant contexts according to the Peres-Shield tree prunning method. Contexts are ordered by length.
# TODO
### Paralelization
After experimentation the best possible idea for paralelization would be to create different hashmaps for each sunsequence length.
Hashmaps are then joined from longest to smallest.
The hashmap at `max_depth + 1` can be discarded after.
Could be very fast depending on merging algo.
Raw data
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"description": "# Variable Length Markov Model (VLMC)\n\n[](https://pepy.tech/project/vlmc) \n[](https://pypi.org/project/vlmc/)\n\nImplementation of Variable Length Markov Chains (VLMC) for Python.\nSuffix tree building is done top-down using the [Peres-Shield](https://link.springer.com/chapter/10.1007/11557067_24) order estimation method.\nIt is written in Rust with Python Bindings.\n\n##### Contents\n - [Installation](#installation)\n * [Compiling from source](#compilation-from-source) \n - [Usage](#usage)\n - [`fit`](#fit)\n - [`suffix`](#get_suffix)\n - [`counts`](#get_counts)\n - [`distribution`](#get_distribution)\n - [`contexts`](#get_contexts)\n - [Future](#todo)\n\n\n## Installation\n\nPre-built packages for many Linux, Windows, and OSX systems are available\nin [PyPI](https://pypi.org/project/vlmc/) and can be installed with:\n\n```sh\npip install vlmc\n```\nOn uncommon architectures, you may need to first\n[install Cargo](https://doc.rust-lang.org/cargo/getting-started/installation.html) before running `pip install vlmc`.\n### Compilation from source\n\nIn order to compile from source you will need to [install Rust/Cargo](https://doc.rust-lang.org/cargo/getting-started/installation.html) and [maturin](https://github.com/PyO3/maturin#maturin) for the python bindings.\nMaturin is best used within a Python virtual environment:\n\n```sh\n# activate your desired virtual environment first, then:\npip install maturin\ngit clone https://github.com/antonio-leitao/vlmc.git\ncd vlmc\n# build and install the package:\nmaturin develop --release\n```\n\n# Usage\n\n```python\nimport vlmc\ntree = vlmc.VLMC(max_depth, alphabet_size, n_jobs=-1)\n```\nParameters:\n- `max_depth`: Maximum depth of tree. Subsequences whose length exceed the `max_depth` will not be considered nor counted. \n- `alphabet_size`: Total number of symbols in the alphabet. This number has to be bigger than the highest integer encountered, else it will cause runtime errors. \n- `n_jobs`: Number of subprocesses to spawn when running the vlmc. Choose `-1` for using all available processes. \n\n### `fit`\n\n> **Note**\n> fit method returns `None` and not `self`. This is by design as to not expose the rust object to python.\n\n```python\ndata = [\n [1,2,3],\n [2,3],\n [1,0,1],\n [2]\n]\n\ntree.fit(data)\n```\n\nArguments:\n- `data`: List of lists containing sequences of discrete values to fit on. Values are assumed to be integers form `0` to `alphabet_size`. List is expected to be two dimensional.\n\n### `get_suffix`\nGiven a sequence, returns the longest suffix that is present in the VLMC.\n\n```python\nsuffix = tree.get_suffix(sequence)\n```\nArguments:\n- `sequence`: list of integers representing a sequence of discrete varaibles. \n\nReturns:\n- `suffix` : longest suffix of sequence that is present in the VLMC. \n\n### `get_counts`\nGets the total number of occurences of a given sequence of integers.\nWill throw a `KeyError` if the sequence is not a tree node. Consider using `get_suffix` to make sure to get a tree node.\n\n```python\ncounts = tree.get_counts(sequence)\n```\nArguments:\n- `sequence`: list of integers representing a sequence of discrete varaibles.\n \nReturns:\n- `counts` : integer \n\n### `get_distribution`\nGets the vector of probabilities over the entire alphabet for the given sequence.\nWill throw a `KeyError` if the sequence is not a tree node. Consider using `get_suffix` to make sure to get a tree node.\n\n```python\nprobabilities = tree.get_distribution(sequence)\n```\nArguments:\n- `sequence`: list of integers representing a sequence of discrete variables. \n\nReturns:\n- `probabilities` : list of floats representing the probability of observing a specific state (index) as the next symbol.\n\n### `get_contexts`\n\n```python\ncontexts = tree.get_contexts()\n```\nReturns:\n- `contexts`: list of relevant contexts according to the Peres-Shield tree prunning method. Contexts are ordered by length.\n\n# TODO\n### Paralelization\nAfter experimentation the best possible idea for paralelization would be to create different hashmaps for each sunsequence length.\nHashmaps are then joined from longest to smallest.\nThe hashmap at `max_depth + 1` can be discarded after.\nCould be very fast depending on merging algo.\n\n\n",
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