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

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

.. _sphx_glr_auto_examples_metrics_plot_autc.py:


AUTC
-------------------------

Historgram and Metrics for random scores with different delta.

.. GENERATED FROM PYTHON SOURCE LINES 8-16

.. code-block:: Python
   :lineno-start: 9


    import torch
    import numpy as np
    import matplotlib.pyplot as plt

    from pytorch_ood.utils.metrics import binary_clf_curve
    from pytorch_ood.utils import OODMetrics








.. GENERATED FROM PYTHON SOURCE LINES 17-18

Parameters

.. GENERATED FROM PYTHON SOURCE LINES 18-32

.. code-block:: Python
   :lineno-start: 19


    # delta between in and ood data
    near_delta = 2
    far_delta = 10

    # split
    in_samples_num = 9
    out_samples_num = 1

    # random torch tensors
    offset = 10**3
    in_scores = torch.rand(in_samples_num * offset)
    out_scores = torch.rand(out_samples_num * offset)








.. GENERATED FROM PYTHON SOURCE LINES 33-34

Define function

.. GENERATED FROM PYTHON SOURCE LINES 34-84

.. code-block:: Python
   :lineno-start: 36



    def metrics_and_plots(in_scores, out_scores, delta, name):
        metrics = OODMetrics()
        # concat all scores
        scores = torch.cat([in_scores, out_scores + delta])
        # create labels
        labels = torch.cat([torch.zeros_like(in_scores), torch.ones_like(out_scores)])
        metrics.update(scores, -labels)
        metric_dict = metrics.compute()
        print(name, metric_dict)

        # Create a single figure with two subplots
        fig, axes = plt.subplots(1, 2, figsize=(12, 5))

        # Plot histogram
        axes[0].hist(
            in_scores.cpu().numpy(),
            bins=100,
            alpha=0.5,
            label="In-Distribution",
            color="tab:blue",
        )
        axes[0].hist(
            (out_scores + delta).cpu().numpy(),
            bins=100,
            alpha=0.5,
            label="Out-of-Distribution",
            color="tab:orange",
        )
        axes[0].set_title(f"{name} Histogram", weight="bold")
        axes[0].set_xlabel("Scores")
        axes[0].set_ylabel("Frequency")
        axes[0].legend(loc="upper right")

        # Plot FPR and FNR curve
        fpr, tpr, thresholds = binary_clf_curve(labels, scores)
        axes[1].plot(thresholds, fpr, label="FPR", color="tab:blue")
        axes[1].plot(thresholds, 1 - tpr, label="FNR", color="tab:orange")
        axes[1].set_title(f"{name} FPR and FNR", weight="bold")
        axes[1].set_xlabel("Thresholds")
        axes[1].set_ylabel("Rate")
        axes[1].legend(loc="best")

        # Adjust layout and save
        plt.tight_layout()
        plt.savefig(f"{name}_metrics_plots.png")
        plt.show()









.. GENERATED FROM PYTHON SOURCE LINES 85-86

Plot and calculate metrics

.. GENERATED FROM PYTHON SOURCE LINES 86-88

.. code-block:: Python
   :lineno-start: 86

    metrics_and_plots(in_scores, out_scores, near_delta, "Near")
    metrics_and_plots(in_scores, out_scores, far_delta, "Far")



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


    *

      .. image-sg:: /auto_examples/metrics/images/sphx_glr_plot_autc_001.png
         :alt: Near Histogram, Near FPR and FNR
         :srcset: /auto_examples/metrics/images/sphx_glr_plot_autc_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/metrics/images/sphx_glr_plot_autc_002.png
         :alt: Far Histogram, Far FPR and FNR
         :srcset: /auto_examples/metrics/images/sphx_glr_plot_autc_002.png
         :class: sphx-glr-multi-img


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

 .. code-block:: none

    Near {'AUROC': 0.9999999403953552, 'AUTC': 0.16641604900360107, 'AUPR-IN': 1.0, 'AUPR-OUT': 0.9999999403953552, 'FPR95TPR': 0.0}
    Far {'AUROC': 1.0, 'AUTC': 0.04535288363695145, 'AUPR-IN': 1.0, 'AUPR-OUT': 1.0, 'FPR95TPR': 0.0}





.. _sphx_glr_download_auto_examples_metrics_plot_autc.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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