.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "glr_examples/applications/plot_flow_rate_prediction.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_glr_examples_applications_plot_flow_rate_prediction.py>`
        to download the full example code or to run this example in your browser via Binder

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

.. _sphx_glr_glr_examples_applications_plot_flow_rate_prediction.py:


=========================================
Predict FR from DC-Resistivity data  
=========================================

shows some steps for predicting flow rate(FR) from 
DC-ERP and VES data 

.. GENERATED FROM PYTHON SOURCE LINES 9-12

.. code-block:: Python

    # Author: L.Kouadio 
    # Licence: BSD-3-clause








.. GENERATED FROM PYTHON SOURCE LINES 13-14

Import the required modules 

.. GENERATED FROM PYTHON SOURCE LINES 14-22

.. code-block:: Python

    from watex.datasets import fetch_data 
    from watex.datasets import load_gbalo, load_tankesse, load_bagoue
    from watex.methods import ResistivityProfiling, VerticalSounding
    from watex.methods import DCProfiling, DCSounding
    from watex.view.plot import QuickPlot 
    from watex.models import pModels
    from watex.exlib import train_test_split, accuracy_score








.. GENERATED FROM PYTHON SOURCE LINES 23-32

The raw DC data is recorded in zenodo   during the drinking water supply 
campaign (DWSC) in 2012-2014 in `Cote d'Ivoire <https://en.wikipedia.org/wiki/Ivory_Coast>`_ 
in partnership with global organizations. The objective was 
to supply 2000 villages with potable water. The geophysical companies 
were associated with drilling ventures to locate the best position to carry 
out the drilling operations. The data is free of charge and can be distributed 
to a third party provided that it cites the authors as a reference. 
First, I randomly fetch the raw DC profiling and sounding data of one of 
these localities (Gbalo) during the DWSC project as:

.. GENERATED FROM PYTHON SOURCE LINES 32-36

.. code-block:: Python


    gdata= load_gbalo () 
    gdata.head(2)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <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>station</th>
          <th>resistivity</th>
          <th>longitude</th>
          <th>latitude</th>
          <th>easting</th>
          <th>northing</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>0.0</td>
          <td>1101</td>
          <td>0.0</td>
          <td>0.0</td>
          <td>790752</td>
          <td>1092750.0</td>
        </tr>
        <tr>
          <th>1</th>
          <td>10.0</td>
          <td>1147</td>
          <td>0.0</td>
          <td>0.0</td>
          <td>790747</td>
          <td>1092758.0</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 37-51

Secondly, I will compute the DC prediction parameters by calling the 
appropriate methods. For demonstration, I assume that the drilling is 
performed at station 5(S05) on the survey line, i.e the DC parameters are 
computed at that station. However, if the station is not specified, 
the algorithm will find the best conductive zone based on the resistivity 
values and will store the value in attribute :attr:`sves_` (position to locate a drill). 
The auto-detection can be used when users need to propose a place to make a 
drill.  Note that for a real case study, it is recommended to specify the 
station where the drilling operation was performed through the parameter 
station. For instance, automatic drilling location detection can predict a 
station located in a marsh area that is not a suitable place for making a 
drill. Therefore, to avoid any misinterpretation due to the complexity of 
the geological area, it is useful to provide the station position. The 
code snippets are:

.. GENERATED FROM PYTHON SOURCE LINES 51-55

.. code-block:: Python


    erpo = ResistivityProfiling (station = 'S05').fit(gdata )
    erpo.conductive_zone_  #(1) 





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

 .. code-block:: none


    array([1345, 1369, 1406, 1543, 1480, 1517, 1754])



.. GENERATED FROM PYTHON SOURCE LINES 56-58

.. code-block:: Python

    erpo.summary(keep_params=True,return_table= True) #(2) 






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <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>power</th>
          <th>magnitude</th>
          <th>shape</th>
          <th>type</th>
          <th>sfi</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>60</td>
          <td>409</td>
          <td>K</td>
          <td>PC</td>
          <td>0.926003</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 59-64

`#(1)` shows the resistivity of the best conductive zone and `#(2)` returns 
the main prediction parameters.   For reading multiple ERP data, it is 
suggested to use the :class:`~watex.methods.DCProfiling` method. It performs 
the same task but each parameter is stored in a line object. Let's go ahead  
by fetching another locality of ERP data (**Tankesse**) for demonstration:

.. GENERATED FROM PYTHON SOURCE LINES 64-68

.. code-block:: Python

    
    dcpo= DCProfiling (stations =['S05', 'S07'] ).fit(gdata, load_tankesse() ) 
    dcpo.nlines_ #(3)





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

 .. code-block:: none


    dc-erp  :   0%|                                         | 0/2 [00:00<?, ?B/s]
    dc-erp  : 100%|################################| 2/2 [00:00<00:00, 170.12B/s]

    2



.. GENERATED FROM PYTHON SOURCE LINES 69-71

.. code-block:: Python

    dcpo.line1.conductive_zone_ #(4)
 




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

 .. code-block:: none


    array([1345, 1369, 1406, 1543, 1480, 1517, 1754])



.. GENERATED FROM PYTHON SOURCE LINES 72-74

.. code-block:: Python

    dcpo.line1.sfi_ 





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

 .. code-block:: none


    array([0.92600271])



.. GENERATED FROM PYTHON SOURCE LINES 75-83

`#(3)` shows the number of the given lines (line 1 for ERP **Gbalo** and 
line 2 for ERP **Tankesse**. The line 1 value in `#(4)` computed from 
multiple DC-profiling is similar to the individual computation of the 
same line in (1) although the first approach gives multiple other features 
such as the conductive zone visualization with the plotAnomaly method/function. 
The same scheme for sounding parameter computation can be done with 
the :class:`~watex.methods.VerticalSounding` and :class:`~watex.methods.electrical.DCSounding`. 
Note that the latter saves data into a site object and not in line. For instance:

.. GENERATED FROM PYTHON SOURCE LINES 83-90

.. code-block:: Python

    

    gvdata= load_gbalo (kind ='ves') 
    veso= VerticalSounding (search= 45 ).fit(gvdata)
    dcvo = DCSounding(search=45).fit(gvdata)
    veso.ohmic_area_





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

 .. code-block:: none


    dc-ves  :   0%|                                         | 0/1 [00:00<?, ?B/s]
    dc-ves  : 100%|################################| 1/1 [00:00<00:00, 156.43B/s]

    268.0877145032066



.. GENERATED FROM PYTHON SOURCE LINES 91-93

.. code-block:: Python


    dcvo.site1.ohmic_area_ 




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

 .. code-block:: none


    268.0877145032066



.. GENERATED FROM PYTHON SOURCE LINES 94-104

The `search` parameter passed to the above class is useful to find water
outside of the pollution. Usually, when the VES  is performed, we are 
expecting groundwater in the fractured rock in deeper that is outside of 
any anthropic pollution (Biemi, 1992). Thus, the search parameter indicates 
where the search of the fracture zone in deeper must start. For instance, 
``search=45`` tells the algorithm to start detecting the fracture zone from 45m 
to deeper (Figure below). In addition, when computing the prediction parameter 
like ohmic-area (ohmS) of multiple sounding data for prediction purposes, 
it is strongly recommended to settle the search argument unchangeable 
for all sounding sites. 

.. GENERATED FROM PYTHON SOURCE LINES 104-107

.. code-block:: Python


    veso.plotOhmicArea(fbtw=True , style ='dark_background')




.. image-sg:: /glr_examples/applications/images/sphx_glr_plot_flow_rate_prediction_001.png
   :alt: Vertical Electrical Sounding
   :srcset: /glr_examples/applications/images/sphx_glr_plot_flow_rate_prediction_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 108-116

Furthermore, the DC parameters from the :meth:`~watex.methods.VerticalSounding.summary` 
methods are combined with the geological data of the survey area to 
compose the predictor :math:`X`. One of the interesting features of computing the 
DC parameters for prediction purposes is to discuss the features. 
This is possible with the :meth:`~watex.view.QuickPlot.discussingfeatures` 
method of :class:`~watex.view.QuickPlot`. An example of code snippets for 
discussing plot is given below using the complete Bagoue DC parameters  
computed from 431 boreholes:: 

.. GENERATED FROM PYTHON SOURCE LINES 116-131

.. code-block:: Python


    data = load_bagoue ().frame 
    qkObj = QuickPlot(  leg_kws={'loc':'upper right'},
              fig_title = '`sfi` vs`ohmS|`geol`',
                ) 
    qkObj.tname='flow' # target the DC-flow rate prediction dataset
    qkObj.mapflow=True  # to hold category FR0, FR1 etc..
    qkObj.fit(data) 
    sns_pkws={'aspect':2 , 
              "height": 2, 
                      }
    map_kws={'edgecolor':"w"}   
    qkObj.discussingfeatures(features =['ohmS', 'sfi','geol', 'flow'],
                               map_kws=map_kws,  **sns_pkws
                             ) 



.. image-sg:: /glr_examples/applications/images/sphx_glr_plot_flow_rate_prediction_002.png
   :alt: geol = VOLCANO-SEDIM. SCHISTS, geol = GRANITES, geol = GEOSYN. GRANITES, geol = GNEISS-MIGMATITES
   :srcset: /glr_examples/applications/images/sphx_glr_plot_flow_rate_prediction_002.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    QuickPlot(savefig= None, fig_num= 1, fig_size= (12, 8), ... , classes= None, tname= flow, mapflow= True)



.. GENERATED FROM PYTHON SOURCE LINES 132-149

As a comment of discussing features above. Figure shows  at a glance  
that most of the drillings carried out on granites have an `FR` of around 
:math:`1 m^3/hr` (FR1: 0< FR <=1). With these kinds of 
flows, it is obvious that the boreholes will be unproductive (unsustainable) 
within a few years.  However, the volcano-sedimentary schists seem the most 
suitable geological structure with an `FR` greater than :math:`3m^3/hr`. 
However, the wide fractures on these formations (explained by `ohmS > 1500`) 
do not mean that they should be more productive since all of the drillings performed 
on the wide fracture do not always give a productive FR ( :math:`FR>3m^3/hr`) 
contrary to the narrow fractures (around `1000 ohmS`). As a result, it is reliable to 
consider this approach during a future DWSC such as the geology of the area 
and also the rock fracturing ratio computed thanks to the parameters 
sfi and ohmS.  
The following examples demonstrate how to predict FR with a complete 
preprocessed dataset of DC parameters. The pre-trained models (optimal 
model with acceptable variance and bias) of :class:`~watex.models.pModels` 
can be used to predict FR as: 

.. GENERATED FROM PYTHON SOURCE LINES 149-158

.. code-block:: Python

    

    X, y = fetch_data ('bagoue prepared data')
    X_train, X_test, y_train, y_test = train_test_split (
        X, y, test_size =0.2 ) 
    pmo = pModels (model='svm', kernel ='poly').fit (X_train, y_train ) 
    y_pred =  pmo.predict (X_test ) 
    accuracy_score (y_test, y_pred )





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

 .. code-block:: none


    0.7246376811594203



.. GENERATED FROM PYTHON SOURCE LINES 159-161

Note the pre-trained estimator is stored in an attribute :attr:`estimator_`. 
For instance, the pre-trained SVM model can be retrieved using 

.. GENERATED FROM PYTHON SOURCE LINES 161-163

.. code-block:: Python

    pmo.estimator_ 






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>SVC(C=128.0, coef0=7, degree=5, gamma=0.00048828125, kernel=&#x27;poly&#x27;, tol=0.01)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">SVC</label><div class="sk-toggleable__content"><pre>SVC(C=128.0, coef0=7, degree=5, gamma=0.00048828125, kernel=&#x27;poly&#x27;, tol=0.01)</pre></div></div></div></div></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 164-165

or 

.. GENERATED FROM PYTHON SOURCE LINES 165-168

.. code-block:: Python


    pmo.SVM.poly.best_estimator_






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-2" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>SVC(C=128.0, coef0=7, degree=5, gamma=0.00048828125, kernel=&#x27;poly&#x27;, tol=0.01)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" checked><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">SVC</label><div class="sk-toggleable__content"><pre>SVC(C=128.0, coef0=7, degree=5, gamma=0.00048828125, kernel=&#x27;poly&#x27;, tol=0.01)</pre></div></div></div></div></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 169-170

If the model is not a kernel machine, the kernel attribute is discarded instead. For example:

.. GENERATED FROM PYTHON SOURCE LINES 170-176

.. code-block:: Python

    pmo=pModels(model ='xgboost').fit (X_train, y_train )  

    # where xgboost stands for extreme gradient boosting machine(Friedman, 2001) 
    # and  the pre-trained estimator could be retrieved as 
    pmo.XGB.best_estimator_
 





.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-3" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>XGBClassifier(base_score=None, booster=&#x27;gbtree&#x27;, callbacks=None,
                  colsample_bylevel=None, colsample_bynode=None,
                  colsample_bytree=None, device=None, early_stopping_rounds=None,
                  enable_categorical=False, eval_metric=None, feature_types=None,
                  gamma=1.5, grow_policy=None, importance_type=None,
                  interaction_constraints=None, learning_rate=0.07, max_bin=None,
                  max_cat_threshold=None, max_cat_to_onehot=None,
                  max_delta_step=None, max_depth=2, max_leaves=None,
                  min_child_weight=None, missing=nan, monotone_constraints=None,
                  multi_strategy=None, n_estimators=300, n_jobs=None,
                  num_parallel_tree=None, objective=&#x27;multi:softprob&#x27;, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" checked><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">XGBClassifier</label><div class="sk-toggleable__content"><pre>XGBClassifier(base_score=None, booster=&#x27;gbtree&#x27;, callbacks=None,
                  colsample_bylevel=None, colsample_bynode=None,
                  colsample_bytree=None, device=None, early_stopping_rounds=None,
                  enable_categorical=False, eval_metric=None, feature_types=None,
                  gamma=1.5, grow_policy=None, importance_type=None,
                  interaction_constraints=None, learning_rate=0.07, max_bin=None,
                  max_cat_threshold=None, max_cat_to_onehot=None,
                  max_delta_step=None, max_depth=2, max_leaves=None,
                  min_child_weight=None, missing=nan, monotone_constraints=None,
                  multi_strategy=None, n_estimators=300, n_jobs=None,
                  num_parallel_tree=None, objective=&#x27;multi:softprob&#x27;, ...)</pre></div></div></div></div></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 177-178

Let make a new prediction with XGB 

.. GENERATED FROM PYTHON SOURCE LINES 178-182

.. code-block:: Python

    y_pred =  pmo.predict (X_test ) 
    accuracy_score (y_test, y_pred )






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

 .. code-block:: none


    0.8115942028985508




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

   **Total running time of the script:** (0 minutes 1.652 seconds)


.. _sphx_glr_download_glr_examples_applications_plot_flow_rate_prediction.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/watex/watex/master?urlpath=lab/tree/notebooks/glr_examples/applications/plot_flow_rate_prediction.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

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

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