.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/seaborn/plot_aucplot_script.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_seaborn_plot_aucplot_script.py>`
        to download the full example code or to run this example in your browser via JupyterLite or Binder.

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

.. _sphx_glr_auto_examples_seaborn_plot_aucplot_script.py:


plot_aucplot_script with examples
==========================================

An example showing the :py:func:`~scikitplot.seaborn.aucplot` function
with a scikit-learn classifier (e.g., :py:class:`~sklearn.linear_model.LogisticRegression`) instance.

.. GENERATED FROM PYTHON SOURCE LINES 8-12

.. code-block:: Python


    # Authors: The scikit-plots developers
    # SPDX-License-Identifier: BSD-3-Clause








.. GENERATED FROM PYTHON SOURCE LINES 13-14

Import scikit-plot

.. GENERATED FROM PYTHON SOURCE LINES 14-17

.. code-block:: Python

    import scikitplot.seaborn as sp









.. GENERATED FROM PYTHON SOURCE LINES 18-25

.. code-block:: Python

    ax = sp.aucplot(
        x=[0, 1, 1, 0, 1, 1, 0, 1, 1, 0],
        y=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
        fmt=''
    )





.. image-sg:: /auto_examples/seaborn/images/sphx_glr_plot_aucplot_script_001.png
   :alt: ROC (Receiver Operating Characteristic) Curve
   :srcset: /auto_examples/seaborn/images/sphx_glr_plot_aucplot_script_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 26-34

.. code-block:: Python

    ax = sp.aucplot(
        x=[0, 1, 1, 0, 1, 1, 0, 1, 1, 0],
        y=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
        kind="pr",
        fmt=''
    )





.. image-sg:: /auto_examples/seaborn/images/sphx_glr_plot_aucplot_script_002.png
   :alt: PR (Precision-Recall) Curve
   :srcset: /auto_examples/seaborn/images/sphx_glr_plot_aucplot_script_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 35-49

.. code-block:: Python

    import matplotlib.pyplot as plt
    import numpy as np; np.random.seed(0)  # reproducibility
    import pandas as pd

    from sklearn.datasets import make_classification
    from sklearn.datasets import (
        load_breast_cancer as data_2_classes,
        load_iris as data_3_classes,
        load_digits as data_10_classes,
    )
    from sklearn.linear_model import LogisticRegression
    from sklearn.model_selection import train_test_split









.. GENERATED FROM PYTHON SOURCE LINES 50-53

Load the data
X, y = data_3_classes(return_X_y=True, as_frame=False)
X, y = data_2_classes(return_X_y=True, as_frame=False)

.. GENERATED FROM PYTHON SOURCE LINES 53-60

.. code-block:: Python


    # Generate a sample dataset
    X, y = make_classification(n_samples=5000, n_features=20, n_informative=15,
                              n_redundant=2, n_classes=2, n_repeated=0,
                              class_sep=1.5, flip_y=0.01, weights=[0.85, 0.15],
                              random_state=0)








.. GENERATED FROM PYTHON SOURCE LINES 61-66

.. code-block:: Python

    X_train, X_val, y_train, y_val = train_test_split(
        X, y, stratify=y, test_size=0.2, random_state=0
    )
    np.unique(y)





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

 .. code-block:: none


    array([0, 1])



.. GENERATED FROM PYTHON SOURCE LINES 67-68

Create an instance of the LogisticRegression

.. GENERATED FROM PYTHON SOURCE LINES 68-94

.. code-block:: Python

    model = (
        LogisticRegression(
            # max_iter=int(1e5),
            # C=10,
            # penalty='l1',
            # solver='liblinear',
            class_weight='balanced',
            random_state=0
        )
        .fit(X_train, y_train)
    )
    # Perform predictions
    y_val_prob = model.predict_proba(X_val)
    # Create a DataFrame with predictions
    df = pd.DataFrame({
        "y_true": y_val==1,  # target class (0,1,2)
        "y_score": y_val_prob[:, 1],  # target class (0,1,2)
        # np.argmax
        "y_pred": y_val_prob[:, 1] > 0.5,  # target class (0,1,2)
        # "y_true": np.random.normal(0.5, 0.1, 100).round(),
        # "y_score": np.random.normal(0.5, 0.15, 100),
        # "hue": np.random.normal(0.5, 0.4, 100).round(),
    })
    df







.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>y_true</th>
          <th>y_score</th>
          <th>y_pred</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>False</td>
          <td>0.033725</td>
          <td>False</td>
        </tr>
        <tr>
          <th>1</th>
          <td>True</td>
          <td>0.860583</td>
          <td>True</td>
        </tr>
        <tr>
          <th>2</th>
          <td>False</td>
          <td>0.423101</td>
          <td>False</td>
        </tr>
        <tr>
          <th>3</th>
          <td>False</td>
          <td>0.137295</td>
          <td>False</td>
        </tr>
        <tr>
          <th>4</th>
          <td>False</td>
          <td>0.788645</td>
          <td>True</td>
        </tr>
        <tr>
          <th>...</th>
          <td>...</td>
          <td>...</td>
          <td>...</td>
        </tr>
        <tr>
          <th>995</th>
          <td>False</td>
          <td>0.228034</td>
          <td>False</td>
        </tr>
        <tr>
          <th>996</th>
          <td>False</td>
          <td>0.017187</td>
          <td>False</td>
        </tr>
        <tr>
          <th>997</th>
          <td>True</td>
          <td>0.987892</td>
          <td>True</td>
        </tr>
        <tr>
          <th>998</th>
          <td>False</td>
          <td>0.931136</td>
          <td>True</td>
        </tr>
        <tr>
          <th>999</th>
          <td>False</td>
          <td>0.128248</td>
          <td>False</td>
        </tr>
      </tbody>
    </table>
    <p>1000 rows × 3 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 95-98

.. code-block:: Python

    ax = sp.aucplot(x=df.y_true, y=df.y_score)





.. image-sg:: /auto_examples/seaborn/images/sphx_glr_plot_aucplot_script_003.png
   :alt: ROC (Receiver Operating Characteristic) Curve
   :srcset: /auto_examples/seaborn/images/sphx_glr_plot_aucplot_script_003.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 99-109

.. code-block:: Python

    ax = sp.aucplot(
        df,
        x="y_true",
        y="y_score",
        kind="pr",
        label=f"class 1",
        # fmt=''
    )





.. image-sg:: /auto_examples/seaborn/images/sphx_glr_plot_aucplot_script_004.png
   :alt: PR (Precision-Recall) Curve
   :srcset: /auto_examples/seaborn/images/sphx_glr_plot_aucplot_script_004.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 110-127

.. code-block:: Python

    for i in range(2):
        ax = sp.aucplot(
            x=y_val==i,
            y=y_val_prob[:, i],
            # kind="roc",
            label=f"class {i}",
            # fmt=''
        )

        # --- Collect unique handles and labels ---
        handles, labels = ax.get_legend_handles_labels()
        by_label = dict(zip(labels, handles))  # deduplicate

        # Override legend
        ax.legend(by_label.values(), by_label.keys(), title="Val Dataset")





.. image-sg:: /auto_examples/seaborn/images/sphx_glr_plot_aucplot_script_005.png
   :alt: ROC (Receiver Operating Characteristic) Curve
   :srcset: /auto_examples/seaborn/images/sphx_glr_plot_aucplot_script_005.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 128-155

.. code-block:: Python

    for i in range(2):
        ax = sp.aucplot(
            x=y_val==i,
            y=y_val_prob[:, i],
            kind="pr",
            label=f"class {i}",
            # fmt=''
        )

        # # With raw arrays (no DataFrame)
        # # Works because seaborn normalizes arrays internally
        # np.random.seed(i)  # reproducibility
        # ax = sp.aucplot(
        #     x=np.random.normal(0.5, 0.1, 100).round(),
        #     y=np.random.normal(0.5, 0.1, 100),
        #     kind="pr",
        #     label=f"{i}",
        # )

        # --- Collect unique handles and labels ---
        handles, labels = ax.get_legend_handles_labels()
        by_label = dict(zip(labels, handles))  # deduplicate

        # Override legend
        ax.legend(by_label.values(), by_label.keys(), title="Val Dataset")





.. image-sg:: /auto_examples/seaborn/images/sphx_glr_plot_aucplot_script_006.png
   :alt: PR (Precision-Recall) Curve
   :srcset: /auto_examples/seaborn/images/sphx_glr_plot_aucplot_script_006.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 156-164

.. tags::

   model-type: classification
   model-workflow: model evaluation
   plot-type: line
   plot-type: auc
   level: beginner
   purpose: showcase


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

   **Total running time of the script:** (0 minutes 0.609 seconds)


.. _sphx_glr_download_auto_examples_seaborn_plot_aucplot_script.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-plots/scikit-plots/main?urlpath=lab/tree/notebooks/auto_examples/seaborn/plot_aucplot_script.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/index.html?path=auto_examples/seaborn/plot_aucplot_script.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_aucplot_script.recommendations


.. only:: html

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

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