.. _quantilemlpregressionrst:

=====================
Quantile MLPRegressor
=====================


.. only:: html

    **Links:** :download:`notebook <quantile_mlpregression.ipynb>`, :downloadlink:`html <quantile_mlpregression2html.html>`, :download:`PDF <quantile_mlpregression.pdf>`, :download:`python <quantile_mlpregression.py>`, :downloadlink:`slides <quantile_mlpregression.slides.html>`, :githublink:`GitHub|_doc/notebooks/sklearn/quantile_mlpregression.ipynb|*`


`scikit-learn <http://scikit-learn.org/stable/>`__ does not have a
quantile regression for multi-layer perceptron.
`mlinsights <http://www.xavierdupre.fr/app/mlinsights/helpsphinx/index.html>`__
implements a version of it based on the *scikit-learn* model. The
implementation overwrites method ``_backprop``.

.. code:: ipython3

    %matplotlib inline

.. code:: ipython3

    import warnings
    warnings.simplefilter("ignore")

We generate some dummy data.

.. code:: ipython3

    import numpy
    X = numpy.random.random(1000)
    eps1 = (numpy.random.random(900) - 0.5) * 0.1
    eps2 = (numpy.random.random(100)) * 10
    eps = numpy.hstack([eps1, eps2])
    X = X.reshape((1000, 1))
    Y = X.ravel() * 3.4 + 5.6 + eps

.. code:: ipython3

    from sklearn.neural_network import MLPRegressor
    clr = MLPRegressor(hidden_layer_sizes=(30,), activation='tanh')
    clr.fit(X, Y)




.. parsed-literal::
    MLPRegressor(activation='tanh', hidden_layer_sizes=(30,))



.. code:: ipython3

    from mlinsights.mlmodel import QuantileMLPRegressor
    clq = QuantileMLPRegressor(hidden_layer_sizes=(30,), activation='tanh')
    clq.fit(X, Y)




.. parsed-literal::
    QuantileMLPRegressor(activation='tanh', hidden_layer_sizes=(30,))



.. code:: ipython3

    from pandas import DataFrame
    data= dict(X=X.ravel(), Y=Y, clr=clr.predict(X), clq=clq.predict(X))
    df = DataFrame(data)
    df.head()






.. raw:: html

    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>X</th>
          <th>Y</th>
          <th>clr</th>
          <th>clq</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>0.251734</td>
          <td>6.470634</td>
          <td>7.059780</td>
          <td>6.481283</td>
        </tr>
        <tr>
          <th>1</th>
          <td>0.538065</td>
          <td>7.423694</td>
          <td>8.029974</td>
          <td>7.510084</td>
        </tr>
        <tr>
          <th>2</th>
          <td>0.530510</td>
          <td>7.411181</td>
          <td>8.006414</td>
          <td>7.485186</td>
        </tr>
        <tr>
          <th>3</th>
          <td>0.048348</td>
          <td>5.808051</td>
          <td>6.278572</td>
          <td>5.646920</td>
        </tr>
        <tr>
          <th>4</th>
          <td>0.882162</td>
          <td>8.624456</td>
          <td>8.986741</td>
          <td>8.519049</td>
        </tr>
      </tbody>
    </table>
    </div>



.. code:: ipython3

    import matplotlib.pyplot as plt
    fig, ax = plt.subplots(1, 1, figsize=(10, 4))
    choice = numpy.random.choice(X.shape[0]-1, size=100)
    xx = X.ravel()[choice]
    yy = Y[choice]
    ax.plot(xx, yy, '.', label="data")
    xx = numpy.array([[0], [1]])
    y1 = clr.predict(xx)
    y2 = clq.predict(xx)
    ax.plot(xx, y1, "--", label="L2")
    ax.plot(xx, y2, "--", label="L1")
    ax.set_title("Quantile (L1) vs Square (L2) for MLPRegressor")
    ax.legend();



.. image:: quantile_mlpregression_8_0.png