.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_ch6\ch6_iris.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_ch6_ch6_iris.py>`
        to download the full example code.

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

.. _sphx_glr_auto_ch6_ch6_iris.py:


===============================
6.7. Conditional Sampling
===============================

In this tutorial, we introduce the problem of conditional sampling, i.e., generating samples from a distribution \( p(y|x) \) using kernel-based models.

We’ll use the classic Iris dataset to demonstrate two approaches:
- **Nadaraya-Watson Estimator**
- **Optimal Transport-based Conditioner**

.. GENERATED FROM PYTHON SOURCE LINES 12-118



.. image-sg:: /auto_ch6/images/sphx_glr_ch6_iris_001.png
   :alt: sep.len | pet.wid., sep.wid. | pet.wid., pet.len | pet.wid., sep.len | pet.wid., sep.wid. | pet.wid., pet.len | pet.wid.
   :srcset: /auto_ch6/images/sphx_glr_ch6_iris_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import numpy as np
    import pandas as pd
    import seaborn as sns
    from sklearn.datasets import load_iris

    from codpy import conditioning
    import codpy.core
    from codpy.core import get_matrix
    from codpy.plot_utils import multi_plot
    import matplotlib.pyplot as plt

    def load_iris_data(threshold=0.2):
        """
        Loads the Iris dataset and prepares conditional data:
        using 'petal width' to condition other features.
        """
        data = load_iris()
        feature_names = ["sep.len", "sep.wid.", "pet.len", "pet.wid."]
        df = pd.DataFrame(data.data, columns=feature_names)

        x_cols = ["pet.wid."]
        y_cols = [col for col in df.columns if col not in x_cols]

        x = df[x_cols]
        y = df[y_cols]

        # Identify reference distribution: near the mean of x
        threshold = np.power(threshold, 1.0 / len(x_cols))
        mean_vals = df.mean()
        var_vals = df.var()

        mask = np.ones(len(df), dtype=bool)
        for col in x_cols:
            mask &= np.abs(df[col] - mean_vals[col]) < var_vals[col] * threshold

        ref_dist = df.loc[mask, y_cols]
        return x, y, ref_dist, get_matrix(mean_vals[x_cols]).T


    def sample_and_plot(samplers, N=500):
        """
        Sample conditionally using provided samplers and plot marginals.
        """
        x, y, ref_dist, cond_point = load_iris_data()

        results = []

        for SamplerClass in samplers:
            # Initialize and sample
            model = SamplerClass(x=x, y=y)
            sampled = model.sample(x=cond_point, n=N,iter=0).squeeze()

            # Prepare DataFrame
            df_sampled = pd.DataFrame(sampled, columns=y.columns)
            df_sampled["Type"] = SamplerClass.__name__

            # Combine with reference
            df_ref = ref_dist.copy()
            df_ref["Type"] = "Reference"
            results.append(pd.concat([df_sampled, df_ref]))

        # Plotting
        plot_marginals(results, condition_col=x.columns[0])


    def plot_marginals(results, condition_col):
        """
        Plot cumulative histograms for each feature conditioned on the same input.
        """
        plots = []

        for df in results:
            for col in df.columns:
                if col != "Type":
                    plots.append({"data": df, "col": col, "cond_col": condition_col})

        def plot_func(params, **kwargs):
            ax = params["ax"]
            data, col, cond = params["data"], params["col"], params["cond_col"]
            sns.histplot(
                data=data,
                x=col,
                hue="Type",
                bins=100,
                cumulative=True,
                ax=ax,
                stat="density",
                common_norm=False,
                fill=False,
                element="step",
            )
            ax.set_title(f"{col} | {cond}")

        multi_plot(plots, fun_plot=plot_func, mp_ncols=3, mp_nrows=len(results))


    codpy.core.KerInterface.set_verbose()
    sample_and_plot(
        samplers=[
            conditioning.ConditionerKernel,
            conditioning.NadarayaWatsonKernel,
        ]
    )
    plt.show()
    pass


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

   **Total running time of the script:** (0 minutes 0.270 seconds)


.. _sphx_glr_download_auto_ch6_ch6_iris.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: ch6_iris.ipynb <ch6_iris.ipynb>`

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

      :download:`Download Python source code: ch6_iris.py <ch6_iris.py>`

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

      :download:`Download zipped: ch6_iris.zip <ch6_iris.zip>`


.. only:: html

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

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