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

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

.. _sphx_glr_auto_examples_benchmark_dim_reduction.py:


Benchmark Dimensionality Reduction Methods
==========================================

This script demonstrates how to use the enhanced coco_pipe.dim_reduction module
to benchmark different dimensionality reduction techniques on the Digits dataset.

It compares PCA, UMAP, and Isomap using:
1.  Visualization (2D Embeddings).
2.  Quantitative Metrics (Trustworthiness, Continuity, LCMC).
3.  Shepard Diagrams.

.. GENERATED FROM PYTHON SOURCE LINES 14-92



.. image-sg:: /auto_examples/images/sphx_glr_benchmark_dim_reduction_001.png
   :alt: Dimensionality Reduction Benchmark (Digits Dataset), PCA T=0.82, C=0.94, PCA Shepard Pearson Corr: 0.579, Isomap T=0.87, C=0.97, Isomap Shepard Pearson Corr: 0.592, UMAP T=0.98, C=0.98, UMAP Shepard Pearson Corr: 0.184
   :srcset: /auto_examples/images/sphx_glr_benchmark_dim_reduction_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    Loading Digits dataset...

    Running PCA...
      Trustworthiness: 0.8168
      Continuity:      0.9424
      LCMC:            0.1278

    Running Isomap...
      Trustworthiness: 0.8678
      Continuity:      0.9651
      LCMC:            0.1885

    Running UMAP...
      Trustworthiness: 0.9762
      Continuity:      0.9795
      LCMC:            0.4850

    Benchmark complete. Results plot saved to benchmark_results.png






|

.. code-block:: Python


    import matplotlib.pyplot as plt
    from sklearn.datasets import load_digits
    from sklearn.preprocessing import StandardScaler

    from coco_pipe.dim_reduction import DimReduction
    from coco_pipe.dim_reduction.evaluation.metrics import (
        compute_coranking_matrix,
        continuity,
        lcmc,
        trustworthiness,
    )
    from coco_pipe.viz.dim_reduction import plot_embedding, plot_shepard_diagram


    def main():
        # 1. Load Data
        print("Loading Digits dataset...")
        digits = load_digits()
        X = StandardScaler().fit_transform(digits.data)
        y = digits.target

        # 2. Define Methods to Benchmark
        methods = [
            ("PCA", {"n_components": 2}),
            ("Isomap", {"n_components": 2, "n_neighbors": 30}),
            ("UMAP", {"n_components": 2, "n_neighbors": 15, "min_dist": 0.1}),
        ]

        # 3. Run Benchmark
        results = {}

        fig, axes = plt.subplots(len(methods), 2, figsize=(12, 5 * len(methods)))
        plt.subplots_adjust(hspace=0.4, wspace=0.3)

        for i, (name, kwargs) in enumerate(methods):
            print(f"\nRunning {name}...")

            # Initialize and Fit
            reducer = DimReduction(method=name, **kwargs)
            X_emb = reducer.fit_transform(X)

            # Compute Metrics
            Q = compute_coranking_matrix(X, X_emb)
            t_score = trustworthiness(Q, k=12)
            c_score = continuity(Q, k=12)
            l_score = lcmc(Q, k=12)

            print(f"  Trustworthiness: {t_score:.4f}")
            print(f"  Continuity:      {c_score:.4f}")
            print(f"  LCMC:            {l_score:.4f}")

            results[name] = {"T": t_score, "C": c_score, "L": l_score}

            # Plot Embedding
            ax_emb = axes[i, 0] if len(methods) > 1 else axes[0]
            plot_embedding(
                X_emb,
                labels=y,
                title=f"{name}\nT={t_score:.2f}, C={c_score:.2f}",
                ax=ax_emb,
            )

            # Plot Shepard Diagram
            ax_shep = axes[i, 1] if len(methods) > 1 else axes[1]
            plot_shepard_diagram(
                X, X_emb, sample_size=500, title=f"{name} Shepard", ax=ax_shep
            )

        plt.suptitle("Dimensionality Reduction Benchmark (Digits Dataset)", fontsize=16)

        save_path = "benchmark_results.png"
        plt.savefig(save_path, dpi=150, bbox_inches="tight")
        print(f"\nBenchmark complete. Results plot saved to {save_path}")


    if __name__ == "__main__":
        main()


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

   **Total running time of the script:** (0 minutes 7.855 seconds)


.. _sphx_glr_download_auto_examples_benchmark_dim_reduction.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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