WBN - Initial Release

===
wbn

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Weighted Bayesian Network Text Classification

• Free software: MIT license
• Documentation: https://wbn.readthedocs.io.

Installation

From source

.. code-block:: bash

$ git clone https://github.com/leonkozlowski/wbn.git
$ cd wbn

$ python3.8 -m venv venv
$ pip install -e .

From Build

.. code-block:: bash

$ pip install wbn

Usage

Building, training, and testing WBN

.. code-block:: python

from sklearn.metrics import (
    accuracy_score,
    recall_score,
    precision_score,
)
from sklearn.model_selection import train_test_split

# Import WBN
from wbn.classifier import WBN
from wbn.sample.datasets import load_pr_newswire


# Build the model
wbn = WBN()

# Load a sample dataset
pr_newswire = load_pr_newswire()

# Train/test split
x_train, x_test, y_train, y_test = train_test_split(
    pr_newswire.data, pr_newswire.target, test_size=0.2
)

# Fit the model
wbn.fit(x_train, y_train)

# Testing the model
red = wbn.predict(x_test)

# Reverse encode the labels
y_pred = wbn.reverse_encode(target=pred)

# Calculate key metrics
precision = precision_score(y_test, y_pred, average="weighted")
recall = recall_score(y_test, y_pred, average="weighted")
accuracy = accuracy_score(y_test, y_pred)

Credits

This package was created with Cookiecutter_ and the audreyr/cookiecutter-pypackage_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _audreyr/cookiecutter-pypackage: https://github.com/audreyr/cookiecutter-pypackage