.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples\Trees\DecisionTree.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_Trees_DecisionTree.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_Trees_DecisionTree.py:


Decision tree and random forest classifiers
=============================================

This script evaluates the performance of decision tree and random forest classifiers 
on the Breast Cancer dataset using both DLL (`DLL.MachineLearning.SupervisedLearning.Trees`) 
and scikit-learn.

.. GENERATED FROM PYTHON SOURCE LINES 9-56




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

 .. code-block:: none

    DLL decision tree accuracy: 0.9035087823867798
    DLL decision tree execution time: 2.395894599991152
    SKlearn decision tree accuracy: 0.9122806787490845
    SKlearn decision tree execution time: 0.01156389998504892
    DLL random forest accuracy: 0.9385964870452881
    DLL random forest execution time: 16.5330333999882
    SKlearn random forest accuracy: 0.9649122953414917
    SKlearn random forest execution time: 0.023621800006367266






|

.. code-block:: Python


    import torch
    from sklearn import datasets
    from sklearn import tree
    from sklearn import ensemble
    import time

    from DLL.Data.Metrics import accuracy
    from DLL.Data.Preprocessing import data_split
    from DLL.MachineLearning.SupervisedLearning.Trees import DecisionTree, RandomForestClassifier


    breast_cancer = datasets.load_breast_cancer()

    x = torch.tensor(breast_cancer.data, dtype=torch.float32)
    y = torch.tensor(breast_cancer.target, dtype=torch.float32)
    x_train, y_train, _, _, x_test, y_test = data_split(x, y, train_split=0.8, validation_split=0.0)

    start = time.perf_counter()
    model = DecisionTree(max_depth=1, ccp_alpha=0.0)
    model.fit(x_train, y_train)
    predictions = model.predict(x_test)
    probas = model.predict_proba(x_test)
    print(f"DLL decision tree accuracy: {accuracy(predictions, y_test)}")
    print(f"DLL decision tree execution time: {time.perf_counter() - start}")

    start = time.perf_counter()
    model = tree.DecisionTreeClassifier(max_depth=1, criterion='entropy')
    model.fit(x_train.numpy(), y_train.numpy())
    predictions = model.predict(x_test)
    print(f"SKlearn decision tree accuracy: {accuracy(torch.tensor(predictions), y_test)}")
    print(f"SKlearn decision tree execution time: {time.perf_counter() - start}")

    start = time.perf_counter()
    model = RandomForestClassifier(n_trees=10, max_depth=1)
    model.fit(x_train, y_train)
    predictions = model.predict(x_test)
    probas = model.predict_proba(x_test)
    print(f"DLL random forest accuracy: {accuracy(predictions, y_test)}")
    print(f"DLL random forest execution time: {time.perf_counter() - start}")

    start = time.perf_counter()
    model = ensemble.RandomForestClassifier(n_estimators=10, max_depth=1, criterion='entropy')
    model.fit(x_train.numpy(), y_train.numpy())
    predictions = model.predict(x_test)
    print(f"SKlearn random forest accuracy: {accuracy(torch.tensor(predictions), y_test)}")
    print(f"SKlearn random forest execution time: {time.perf_counter() - start}")


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

   **Total running time of the script:** (0 minutes 23.556 seconds)


.. _sphx_glr_download_auto_examples_Trees_DecisionTree.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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