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

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

.. _sphx_glr_auto_examples_causal_inf.py:


===========================
LinGAM
===========================

https://lingam.readthedocs.io

https://www.kaggle.com/code/sasakitetsuya/water-quality-causal-inference-by-lingam

https://github.com/KJMAN678/casual_reasoning_lignum/blob/master/LIGNAM.ipynb

.. GENERATED FROM PYTHON SOURCE LINES 13-22

.. code-block:: Python

    from IPython.display import Image
    import lingam
    from lingam.utils import make_dot, make_prior_knowledge

    from sklearn.linear_model import LinearRegression
    from sklearn.ensemble import RandomForestRegressor

    from utils import prepare_data








.. GENERATED FROM PYTHON SOURCE LINES 23-26

.. code-block:: Python


    df, _, _ = prepare_data()








.. GENERATED FROM PYTHON SOURCE LINES 27-34

.. code-block:: Python


    df_list = df.columns.to_list()
    df_dict = {}

    for i, column in zip(range(len(df_list)), df_list):
        df_dict[column] = i








.. GENERATED FROM PYTHON SOURCE LINES 35-50

.. code-block:: Python

    prior_knowledge = make_prior_knowledge(
        n_variables=15,
        exogenous_variables = [
                        0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
                        ],
        sink_variables=[df_dict['Efficiency (%)']],
        # paths = [
        #          [df_dict["pH (Max)"], df_dict["pH (Min)"],[df_dict['Temperature\n?C (Min)'],df_dict['Temperature\n?C (Max)'],df_dict['Dissolved Oxygen (mg/L) (Min)'],df_dict['Dissolved Oxygen (mg/L) (Max)']]],
        #          ],     
        no_paths=[(df_dict['Efficiency (%)'], df_dict['Ci (mg/L)']),
              
                  ],
                           
    )








.. GENERATED FROM PYTHON SOURCE LINES 51-60

.. code-block:: Python


    model = lingam.DirectLiNGAM(
                        random_state=42,
                        measure = 'pwling',
                        prior_knowledge = prior_knowledge
                        )

    model.fit(df)





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

 .. code-block:: none


    <lingam.direct_lingam.DirectLiNGAM object at 0x7f2d6a478980>



.. GENERATED FROM PYTHON SOURCE LINES 61-73

.. code-block:: Python


    dot = make_dot(
        model.adjacency_matrix_,
        labels=df.columns.to_list(),
        prediction_target_label='Efficiency (%)'
        )


    dot.format = 'png'
    #dot.render('dag')
    #Image("dag1.png")








.. GENERATED FROM PYTHON SOURCE LINES 74-95

.. code-block:: Python

    prior_knowledge = make_prior_knowledge(
        n_variables=15,
        exogenous_variables = [
                        df_dict["Catalyst type"],
                        df_dict['Catalyst loading (g/L)'],
                        df_dict['Light intensity (W)'],
                        df_dict['time (min)'],
                        df_dict['solution pH'],
                        df_dict['Ci (mg/L)'],
                        df_dict['Anions'],
                        ],
        sink_variables=[df_dict['Efficiency (%)']],
        # paths = [
        #          [df_dict["pH (Max)"], df_dict["pH (Min)"],[df_dict['Temperature\n?C (Min)'],df_dict['Temperature\n?C (Max)'],df_dict['Dissolved Oxygen (mg/L) (Min)'],df_dict['Dissolved Oxygen (mg/L) (Max)']]],
        #          ],     
        no_paths=[(df_dict['Efficiency (%)'], df_dict['Ci (mg/L)']),
              
                  ],
                           
    )








.. GENERATED FROM PYTHON SOURCE LINES 96-105

.. code-block:: Python


    model = lingam.DirectLiNGAM(
                        random_state=42,
                        measure = 'pwling',
                        prior_knowledge = prior_knowledge
                        )

    model.fit(df)





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

 .. code-block:: none


    <lingam.direct_lingam.DirectLiNGAM object at 0x7f2d6a478350>



.. GENERATED FROM PYTHON SOURCE LINES 106-118

.. code-block:: Python


    dot = make_dot(
        model.adjacency_matrix_,
        labels=df.columns.to_list(),
        prediction_target_label='Efficiency (%)'
        )


    dot.format = 'png'
    # dot.render('dag1')
    #Image("dag1.png")








.. GENERATED FROM PYTHON SOURCE LINES 119-138

.. code-block:: Python


    reg = LinearRegression(fit_intercept=True)
    reg.fit(df.drop(["Efficiency (%)"], axis=1), df["Efficiency (%)"])

    features = [i for i in range(df.drop(["Efficiency (%)"], axis=1).shape[1])]
    print("features: ", features)

    dot = make_dot(
        model.adjacency_matrix_,
        labels=df.columns.to_list(),
        prediction_feature_indices=features, 
        prediction_target_label='Efficiency (%)',
        prediction_coefs=reg.coef_
        )

    dot.format = 'png'
    # dot.render('dag_lr')
    dot





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

 .. code-block:: none

    features:  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]

    <graphviz.graphs.Digraph object at 0x7f2d6a47ac60>



.. GENERATED FROM PYTHON SOURCE LINES 139-158

.. code-block:: Python


    reg = RandomForestRegressor()
    reg.fit(df.drop(["Efficiency (%)"], axis=1), df["Efficiency (%)"])

    features = [i for i in range(df.drop(["Efficiency (%)"], axis=1).shape[1])]
    print("features: ", features)

    dot = make_dot(
        model.adjacency_matrix_,
        labels=df.columns.to_list(),
        prediction_feature_indices=features, 
        prediction_target_label='Efficiency (%)',
        prediction_coefs=reg.feature_importances_
        )

    dot.format = 'png'
    # dot.render('dag_rf')
    dot





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

 .. code-block:: none

    features:  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]

    <graphviz.graphs.Digraph object at 0x7f2d6a479520>




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

   **Total running time of the script:** (0 minutes 1.118 seconds)


.. _sphx_glr_download_auto_examples_causal_inf.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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