.. DO NOT EDIT.
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.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/demo_iterative_balancing.py"
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.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_demo_iterative_balancing.py>`
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.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_demo_iterative_balancing.py:


Iterative Balancing Demo
========================

Demonstrates the iterative balancing process for datasets.

.. GENERATED FROM PYTHON SOURCE LINES 7-77




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

 .. code-block:: none

    Demo: Iterative Undersampling with Randomness
    ============================================
    Original Distribution: {'A': 600, 'B': 50}

    --- Balancing with random_state=1 ---
    Balanced Counts: {'B': 50, 'A': 50}
    Class A sample size: 50
    First 5 IDs selected for A: [589 523 393 105 420]

    --- Balancing with random_state=2 ---
    Balanced Counts: {'A': 50, 'B': 50}
    Class A sample size: 50
    First 5 IDs selected for A: [315 296 342 295 144]

    --- Balancing with random_state=3 ---
    Balanced Counts: {'A': 50, 'B': 50}
    Class A sample size: 50
    First 5 IDs selected for A: [164 178 399 231 265]

    --- Overlap Analysis ---
    Overlap between Run 1 and Run 2 (Class A): 5 / 50
    Overlap between Run 1 and Run 3 (Class A): 5 / 50

    SUCCESS: Different seeds produced different subsets!






|

.. code-block:: Python


    import numpy as np
    import pandas as pd

    from coco_pipe.io import DataContainer


    def run_demo():
        print("Demo: Iterative Undersampling with Randomness")
        print("============================================")

        # 1. create imbalanced data: 600 of class 'A', 50 of class 'B'
        n_a = 600
        n_b = 50

        n_total = n_a + n_b
        X = np.random.randn(n_total, 2)
        y = np.array(["A"] * n_a + ["B"] * n_b)
        ids = np.arange(n_total)

        container = DataContainer(X=X, dims=("obs", "feat"), y=y, ids=ids)

        print(f"Original Distribution: {pd.Series(y).value_counts().to_dict()}")

        # 2. Run balance multiple times with different seeds
        seeds = [1, 2, 3]

        # Store indices selected for the majority class 'A' to compare overlaps
        selected_indices_a = []

        for seed in seeds:
            print(f"\n--- Balancing with random_state={seed} ---")
            balanced = container.balance(
                target="y", strategy="undersample", random_state=seed
            )

            # Check counts
            counts = pd.Series(balanced.y).value_counts().to_dict()
            print(f"Balanced Counts: {counts}")

            # Get indices of class A in this balanced set
            # We can find them by looking at IDs or matching logic, but here we can
            # just inspect the subset
            # Since ids are just 0..649, let's see which of the first 600 were picked
            subset_ids = balanced.ids
            class_a_mask = balanced.y == "A"
            ids_a = subset_ids[class_a_mask]

            print(f"Class A sample size: {len(ids_a)}")
            print(f"First 5 IDs selected for A: {ids_a[:5]}")

            selected_indices_a.append(set(ids_a))

        # 3. Analyze overlap
        set1, set2, set3 = selected_indices_a
        overlap_1_2 = len(set1.intersection(set2))
        overlap_1_3 = len(set1.intersection(set3))

        print("\n--- Overlap Analysis ---")
        print(f"Overlap between Run 1 and Run 2 (Class A): {overlap_1_2} / 50")
        print(f"Overlap between Run 1 and Run 3 (Class A): {overlap_1_3} / 50")

        if overlap_1_2 < 50 and overlap_1_3 < 50:
            print("\nSUCCESS: Different seeds produced different subsets!")
        else:
            print("\nFAILURE: Subsets are identical despite different seeds.")


    if __name__ == "__main__":
        run_demo()


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

   **Total running time of the script:** (0 minutes 0.017 seconds)


.. _sphx_glr_download_auto_examples_demo_iterative_balancing.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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