.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/04_performance_metrics/plot_02_roc_curve.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_04_performance_metrics_plot_02_roc_curve.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_04_performance_metrics_plot_02_roc_curve.py:


=======================================
Receiver Operating Characteristic Curve
=======================================

Example of plotting the ROC curve for a classification task.

.. GENERATED FROM PYTHON SOURCE LINES 8-23

.. code-block:: Python


    import matplotlib
    import matplotlib.pyplot as plt
    import numpy as np
    from sklearn.datasets import load_breast_cancer
    from sklearn.metrics import confusion_matrix, roc_auc_score, roc_curve
    from sklearn.preprocessing import StandardScaler

    from sklvq import GMLVQ

    matplotlib.rc("xtick", labelsize="small")
    matplotlib.rc("ytick", labelsize="small")

    data, labels = load_breast_cancer(return_X_y=True)








.. GENERATED FROM PYTHON SOURCE LINES 24-26

Create a GMLVQ object and pass it a distance function, activation function and solver. See the
API reference under documentation for defaults.

.. GENERATED FROM PYTHON SOURCE LINES 26-36

.. code-block:: Python


    model = GMLVQ(
        distance_type="adaptive-squared-euclidean",
        activation_type="swish",
        activation_params={"beta": 2},
        solver_type="waypoint-gradient-descent",
        solver_params={"max_runs": 10, "k": 3, "step_size": np.array([0.1, 0.05])},
        random_state=31415,
    )








.. GENERATED FROM PYTHON SOURCE LINES 37-38

Fit the GMLVQ object to the data and plot the roc curve.

.. GENERATED FROM PYTHON SOURCE LINES 38-76

.. code-block:: Python


    # Object to perform z-transform
    scaler = StandardScaler()

    # Compute (fit) and apply (transform) z-transform
    data = scaler.fit_transform(data)

    # Train the model using the scaled X and true labels
    model.fit(data, labels)

    # Get the decision values (which are used in predict) instead of the labels. The values are with
    # respect to the "greater" class, i.e., index 1.
    label_score = model.decision_function(data)

    # roc_curve expects the y_score to be with respect to the positive class.
    fpr, tpr, thresholds = roc_curve(y_true=labels, y_score=label_score, pos_label=1, drop_intermediate=True)
    roc_auc = roc_auc_score(y_true=labels, y_score=label_score)

    #  Sometimes it is good to know where the Nearest prototype classifier is on this curve. This can
    #  be computed using the confusion matrix function from sklearn.
    tn, fp, fn, tp = confusion_matrix(y_true=labels, y_pred=model.predict(data)).ravel()

    # The tpr and fpr of the npc are then given by:
    npc_tpr = tp / (tp + fn)
    npc_fpr = fp / (fp + tn)

    fig, ax = plt.subplots()
    fig.suptitle("Receiver operating characteristic ")
    # Plot the ROC curve
    ax.plot(fpr, tpr, color="darkorange", lw=2, label=f"ROC AUC = {roc_auc:.3f}")
    # Plot the random line
    ax.plot([0, 1], [0, 1], color="navy", lw=2, linestyle="--")
    # Plot the NPC classifier
    ax.plot(npc_fpr, npc_tpr, color="green", marker="o", markersize="12")
    ax.set_xlabel("False Positive Rate")
    ax.set_ylabel("True Positive Rate")
    ax.legend(loc="lower right")
    ax.grid(False)



.. image-sg:: /auto_examples/04_performance_metrics/images/sphx_glr_plot_02_roc_curve_001.png
   :alt: Receiver operating characteristic 
   :srcset: /auto_examples/04_performance_metrics/images/sphx_glr_plot_02_roc_curve_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.108 seconds)


.. _sphx_glr_download_auto_examples_04_performance_metrics_plot_02_roc_curve.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: plot_02_roc_curve.ipynb <plot_02_roc_curve.ipynb>`

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

      :download:`Download Python source code: plot_02_roc_curve.py <plot_02_roc_curve.py>`

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

      :download:`Download zipped: plot_02_roc_curve.zip <plot_02_roc_curve.zip>`


.. only:: html

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

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