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

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

.. _sphx_glr_auto_examples_plot_dcrt_example.py:


Distilled Conditional Randomization Test (dCRT) using Lasso vs Random Forest learners
=====================================================================================

This example compares the performance of d0crt based on
the lasso (1) and random forest (2) implementations. The number of
repetitions is set to 10. The metrics used are the type-I error and
the power

.. GENERATED FROM PYTHON SOURCE LINES 12-14

Imports needed for this script
------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 14-26

.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np

    from hidimstat.dcrt import dcrt_zero
    from hidimstat.scenario import multivariate_1D_simulation

    plt.rcParams.update({"font.size": 21})

    typeI_error = {"Lasso": [], "Forest": []}
    power = {"Lasso": [], "Forest": []}








.. GENERATED FROM PYTHON SOURCE LINES 27-29

Processing the computations
---------------------------

.. GENERATED FROM PYTHON SOURCE LINES 29-70

.. code-block:: Python


    for sim_ind in range(10):
        print(f"Processing: {sim_ind+1}")
        np.random.seed(sim_ind)

        # Number of observations
        n = 1000
        # Number of variables
        p = 10
        # Number of relevant variables
        n_signal = 2
        # Signal-to-noise ratio
        snr = 4
        # Correlation coefficient
        rho = 0.8
        # Nominal false positive rate
        alpha = 5e-2

        X, y, _, __ = multivariate_1D_simulation(
            n_samples=n, n_features=p, support_size=n_signal, rho=rho, seed=sim_ind
        )

        # Applying a reLu function on the outcome y to get non-linear relationships
        y = np.maximum(0.0, y)

        ## dcrt Lasso ##
        results_lasso = dcrt_zero(X, y, screening=False, verbose=True)
        typeI_error["Lasso"].append(
            sum(results_lasso[1][n_signal:] < alpha) / (p - n_signal)
        )
        power["Lasso"].append(sum(results_lasso[1][:n_signal] < alpha) / (n_signal))

        ## dcrt Random Forest ##
        results_forest = dcrt_zero(
            X, y, screening=False, statistic="randomforest", verbose=True
        )
        typeI_error["Forest"].append(
            sum(results_forest[1][n_signal:] < alpha) / (p - n_signal)
        )
        power["Forest"].append(sum(results_forest[1][:n_signal] < alpha) / (n_signal))





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

 .. code-block:: none

    Processing: 1
    Processing: 2
    Processing: 3
    Processing: 4
    Processing: 5
    Processing: 6
    Processing: 7
    Processing: 8
    Processing: 9
    Processing: 10




.. GENERATED FROM PYTHON SOURCE LINES 71-73

Plotting the comparison
-----------------------

.. GENERATED FROM PYTHON SOURCE LINES 73-86

.. code-block:: Python


    fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(15, 10))
    ax[0].set_title("Type-I Error")
    ax[0].boxplot(typeI_error.values())
    ax[0].set_xticklabels(typeI_error.keys())
    ax[0].axhline(linewidth=1, color="r")

    ax[1].set_title("Power")
    ax[1].boxplot(power.values())
    ax[1].set_xticklabels(power.keys())
    ax[1].set_ylim(0.5, 1)

    plt.show()



.. image-sg:: /auto_examples/images/sphx_glr_plot_dcrt_example_001.png
   :alt: Type-I Error, Power
   :srcset: /auto_examples/images/sphx_glr_plot_dcrt_example_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (1 minutes 2.874 seconds)

**Estimated memory usage:**  212 MB


.. _sphx_glr_download_auto_examples_plot_dcrt_example.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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