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

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

.. _sphx_glr_auto_examples_UnsupervisedLearning_DimensionalityReduction.py:


Comparison of dimensionality reduction algorithms
===============================================================

This script evaluates and visualizes various dimensionality reduction algorithms on the iris dataset. 
For each algorithm, a visualization of the latent space, which is used for comparison of the algorithms.

.. GENERATED FROM PYTHON SOURCE LINES 8-73



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /auto_examples/UnsupervisedLearning/images/sphx_glr_DimensionalityReduction_001.png
         :alt: PCA, Robust PCA, LDA, T-SNE, UMAP | min_dist=0.1, UMAP | min_dist=2
         :srcset: /auto_examples/UnsupervisedLearning/images/sphx_glr_DimensionalityReduction_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/UnsupervisedLearning/images/sphx_glr_DimensionalityReduction_002.png
         :alt: DimensionalityReduction
         :srcset: /auto_examples/UnsupervisedLearning/images/sphx_glr_DimensionalityReduction_002.png
         :class: sphx-glr-multi-img





.. code-block:: Python

    import torch
    import matplotlib.pyplot as plt
    from sklearn import datasets

    from DLL.MachineLearning.UnsupervisedLearning.DimensionalityReduction import PCA, LDA, RobustPCA, TSNE, UMAP


    # import tensorflow as tf
    # (images, labels), (_, _) = tf.keras.datasets.mnist.load_data()
    # X = torch.from_numpy(images).to(dtype=torch.float64).reshape(60000, -1)
    # y = torch.from_numpy(labels).to(dtype=torch.int32)
    iris = datasets.load_iris()
    X = torch.tensor(iris.data, dtype=torch.float32)
    y = torch.tensor(iris.target, dtype=torch.float32)
    # X[0, 0] = 100
    # X[1, 3] = 100
    # breast_cancer = datasets.load_breast_cancer()
    # X = torch.tensor(breast_cancer.data, dtype=torch.float32)
    # y = torch.tensor(breast_cancer.target, dtype=torch.float32)

    transformer_pca = PCA(n_components=2)
    reduced_pca = transformer_pca.fit_transform(X, normalize=False)

    transformer_UMAP1 = UMAP(n_components=2, init="spectral", p=1, n_neighbor=30, min_dist=0.1, learning_rate=1)
    reduced_UMAP1 = transformer_UMAP1.fit_transform(X, epochs=300)

    transformer_lda = LDA(n_components=2)
    reduced_lda = transformer_lda.fit_transform(X, y)

    transformer_robustPCA = RobustPCA(n_components=2, method="mcd")
    reduced_robustPCA = transformer_robustPCA.fit_transform(X, epochs=10)

    transformer_TSNE = TSNE(n_components=2, init="random", p=2, early_exaggeration=1, perplexity=10)
    reduced_TSNE = transformer_TSNE.fit_transform(X, epochs=50)

    transformer_UMAP2 = UMAP(n_components=2, init="spectral", p=1, n_neighbor=30, min_dist=2, learning_rate=1)
    reduced_UMAP2 = transformer_UMAP2.fit_transform(X, epochs=300)

    fig, axes = plt.subplots(3, 2, figsize=(8, 12))
    plt.subplots_adjust(hspace=0.3, wspace=0.3)
    axes = axes.ravel()
    axes[0].scatter(reduced_pca[:, 0], reduced_pca[:, 1], c=y, s=5)
    axes[0].set_title("PCA")
    # axes[0].set_xlim((-5, 5))
    # axes[0].set_ylim((-5, 5))
    axes[1].scatter(reduced_robustPCA[:, 0], reduced_robustPCA[:, 1], c=y, s=5)
    axes[1].set_title("Robust PCA")
    # axes[1].set_xlim((-5, 5))
    # axes[1].set_ylim((-5, 5))
    axes[2].scatter(reduced_lda[:, 0], reduced_lda[:, 1], c=y, s=5)
    axes[2].set_title("LDA")
    axes[3].scatter(reduced_TSNE[:, 0], reduced_TSNE[:, 1], c=y, s=5)
    axes[3].set_title("T-SNE")
    axes[4].scatter(reduced_UMAP1[:, 0], reduced_UMAP1[:, 1], c=y, s=5)
    axes[4].set_title("UMAP | min_dist=0.1")
    axes[5].scatter(reduced_UMAP2[:, 0], reduced_UMAP2[:, 1], c=y, s=5)
    axes[5].set_title("UMAP | min_dist=2")

    plt.figure(figsize=(8, 8))
    plt.semilogy(transformer_TSNE.history, label="T-SNE (KL Divergence)")
    plt.semilogy(transformer_UMAP2.history, label="UMAP (Cross entropy)")
    plt.xlabel("Epoch")
    plt.ylabel("loss")
    plt.legend()
    plt.show()


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

   **Total running time of the script:** (0 minutes 4.188 seconds)


.. _sphx_glr_download_auto_examples_UnsupervisedLearning_DimensionalityReduction.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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