.. _sphx_glr_auto_examples_balance_ex_undersample.py:


==============================
Undersampling minority samples
==============================

This example creates an imbalanced classification dataset, and
under-samples it to balance the class ratios. Under-sampling is
typically only possible if your dataset is large enough to
facilitate losing some samples from the majority class. If not,
you may later suffer from a high variance problem.

.. raw:: html

   <br/>





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

 Out::

    Num zero class (pre-balance): 513
    Num one class (pre-balance): 4487

    Num zero class (post-balance): 513
    Num one class (post-balance): 2565
    Num samples (post-balance): 3078
                 0  ...        19
    3475  1.291530  ...  0.193104
    497  -1.520067  ... -3.660372
    2767  2.465927  ... -2.666850
    3226 -0.161823  ... -0.314925
    4905  0.103988  ...  0.427975

    [5 rows x 20 columns]




|


.. code-block:: python

    print(__doc__)

    # Author: Taylor Smith <taylor.smith@alkaline-ml.com>

    from sklearn.datasets import make_classification
    from skoot.balance import under_sample_balance
    import pandas as pd

    # #############################################################################
    # Create an imbalanced dataset
    X, y = make_classification(n_samples=5000, n_classes=2, weights=[0.1, 0.9],
                               random_state=42)

    # get counts:
    zero_mask = y == 0
    print("Num zero class (pre-balance): %i" % zero_mask.sum())
    print("Num one class (pre-balance): %i\n" % (~zero_mask).sum())

    # #############################################################################
    # Balance the dataset
    X_balance, y_balance = under_sample_balance(X, y, balance_ratio=0.2,
                                                random_state=42)

    # get the new counts
    new_mask = y_balance == 0
    print("Num zero class (post-balance): %i" % new_mask.sum())
    print("Num one class (post-balance): %i" % (~new_mask).sum())
    print("Num samples (post-balance): %i" % X_balance.shape[0])

    # #############################################################################
    # This also works for pandas DataFrames

    X_balance_df, _ = under_sample_balance(pd.DataFrame.from_records(X),
                                           y, balance_ratio=0.2,
                                           random_state=42)

    print(X_balance_df.head())

**Total running time of the script:** ( 0 minutes  0.055 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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