.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples\NaiveBayes\NaiveBayes.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_NaiveBayes_NaiveBayes.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_NaiveBayes_NaiveBayes.py:


Naive Bayes Classifiers on Iris and Synthetic Datasets
======================================================

This script demonstrates the use of different Naive Bayes classifiers (Gaussian, Bernoulli, and 
Multinomial) on multiple datasets: the Iris dataset and a synthetic dataset. The classifiers are 
evaluated based on their accuracy in predicting the target values.

.. GENERATED FROM PYTHON SOURCE LINES 9-54




.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    1.0
    0.9333333373069763
    0.8675000071525574






|

.. code-block:: Python

    import torch
    from sklearn import datasets

    from DLL.Data.Preprocessing import data_split
    from DLL.Data.Metrics import accuracy
    from DLL.MachineLearning.SupervisedLearning.NaiveBayes import GaussianNaiveBayes, BernoulliNaiveBayes, MultinomialNaiveBayes


    iris = datasets.load_iris()
    X = torch.tensor(iris.data, dtype=torch.float32)
    y = torch.tensor(iris.target, dtype=torch.float32)
    # X = X[y != 2]
    # y = y[y != 2]

    X_train, y_train, X_test, y_test, _, _ = data_split(X, y, train_split=0.8, validation_split=0.2)

    model = GaussianNaiveBayes()
    model.fit(X_train, y_train)
    predictions = model.predict(X_test)
    # print(model.predict_proba(X_test))
    print(accuracy(predictions, y_test))


    y_bernoulli = (y != 0).to(torch.int32)
    X_bernoulli = (X > torch.mean(X, dim=0)).to(torch.int32)
    X_train, y_train, X_test, y_test, _, _ = data_split(X_bernoulli, y_bernoulli, train_split=0.8, validation_split=0.2)

    model = BernoulliNaiveBayes()
    model.fit(X_train, y_train)
    predictions = model.predict(X_test)
    # print(model.predict_proba(X_test))
    print(accuracy(predictions, y_test))


    n_samples = 2000
    n_features = 10
    X_multinomial = torch.poisson(torch.ones(size=(n_samples, n_features)) * 3)
    y_multinomial = torch.bitwise_or(torch.bitwise_and(X_multinomial[:, 0] >= 5, X_multinomial[:, 1] < 3), (X_multinomial[:, 2] >= 3)).to(torch.float32)
    X_train, y_train, X_test, y_test, _, _ = data_split(X_multinomial, y_multinomial, train_split=0.8, validation_split=0.2)

    model = MultinomialNaiveBayes()
    model.fit(X_train, y_train)
    predictions = model.predict(X_test)
    # print(model.predict_proba(X_test))
    print(accuracy(predictions, y_test))


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 3.145 seconds)


.. _sphx_glr_download_auto_examples_NaiveBayes_NaiveBayes.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: NaiveBayes.ipynb <NaiveBayes.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: NaiveBayes.py <NaiveBayes.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: NaiveBayes.zip <NaiveBayes.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_