.. _contextrst:

================
Useful functions
================


.. only:: html

    **Links:** :download:`notebook <context.ipynb>`, :downloadlink:`html <context2html.html>`, :download:`PDF <context.pdf>`, :download:`python <context.py>`, :downloadlink:`slides <context.slides.html>`, :githublink:`GitHub|_doc/notebooks/context.ipynb|*`


Benchmarks measures performances. It can be useful to know in which
context. These are some functions to know better about it.

.. code:: ipython3

    from jyquickhelper import add_notebook_menu
    add_notebook_menu()






.. contents::
    :local:





.. code:: ipython3

    %matplotlib inline

Machine
-------

.. code:: ipython3

    from pymlbenchmark.context import machine_information
    import numpy
    machine_information(['numpy', 'pandas', 'onnx'])




.. parsed-literal::
    [{'name': 'date', 'version': '2019-03-07'},
     {'name': 'python',
      'value': '3.7.2 (tags/v3.7.2:9a3ffc0492, Dec 23 2018, 23:09:28) [MSC v.1916 64 bit (AMD64)]'},
     {'name': 'platform', 'value': 'win32'},
     {'name': 'OS', 'value': 'Windows-10-10.0.17134-SP0'},
     {'name': 'machine', 'value': 'AMD64'},
     {'name': 'processor',
      'value': 'Intel64 Family 6 Model 78 Stepping 3, GenuineIntel'},
     {'name': 'release', 'value': '10'},
     {'name': 'architecture', 'value': ('64bit', 'WindowsPE')},
     {'name': 'numpy', 'version': '1.16.2'},
     {'name': 'onnx', 'version': 'not-imported'},
     {'name': 'pandas', 'version': '0.24.1'}]



.. code:: ipython3

    import pandas
    pandas.DataFrame(machine_information(['numpy', 'pandas', 'onnx']))






.. 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>name</th>
          <th>value</th>
          <th>version</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>date</td>
          <td>NaN</td>
          <td>2019-03-07</td>
        </tr>
        <tr>
          <th>1</th>
          <td>python</td>
          <td>3.7.2 (tags/v3.7.2:9a3ffc0492, Dec 23 2018, 23...</td>
          <td>NaN</td>
        </tr>
        <tr>
          <th>2</th>
          <td>platform</td>
          <td>win32</td>
          <td>NaN</td>
        </tr>
        <tr>
          <th>3</th>
          <td>OS</td>
          <td>Windows-10-10.0.17134-SP0</td>
          <td>NaN</td>
        </tr>
        <tr>
          <th>4</th>
          <td>machine</td>
          <td>AMD64</td>
          <td>NaN</td>
        </tr>
        <tr>
          <th>5</th>
          <td>processor</td>
          <td>Intel64 Family 6 Model 78 Stepping 3, GenuineI...</td>
          <td>NaN</td>
        </tr>
        <tr>
          <th>6</th>
          <td>release</td>
          <td>10</td>
          <td>NaN</td>
        </tr>
        <tr>
          <th>7</th>
          <td>architecture</td>
          <td>(64bit, WindowsPE)</td>
          <td>NaN</td>
        </tr>
        <tr>
          <th>8</th>
          <td>numpy</td>
          <td>NaN</td>
          <td>1.16.2</td>
        </tr>
        <tr>
          <th>9</th>
          <td>onnx</td>
          <td>NaN</td>
          <td>not-imported</td>
        </tr>
        <tr>
          <th>10</th>
          <td>pandas</td>
          <td>NaN</td>
          <td>0.24.1</td>
        </tr>
      </tbody>
    </table>
    </div>



One benchmark
-------------

.. code:: ipython3

    from pymlbenchmark.datasets import experiment_results
    df = experiment_results('onnxruntime_LogisticRegression')
    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>N</th>
          <th>count</th>
          <th>dim</th>
          <th>fit_intercept</th>
          <th>lib</th>
          <th>lower</th>
          <th>max</th>
          <th>max3</th>
          <th>mean</th>
          <th>median</th>
          <th>method</th>
          <th>min</th>
          <th>min3</th>
          <th>repeat</th>
          <th>upper</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>1</td>
          <td>100</td>
          <td>1</td>
          <td>True</td>
          <td>skl</td>
          <td>-0.000014</td>
          <td>0.000392</td>
          <td>0.000128</td>
          <td>0.000071</td>
          <td>0.000052</td>
          <td>predict</td>
          <td>0.000038</td>
          <td>0.000039</td>
          <td>100</td>
          <td>0.000155</td>
        </tr>
        <tr>
          <th>1</th>
          <td>1</td>
          <td>100</td>
          <td>1</td>
          <td>True</td>
          <td>ort</td>
          <td>-0.000008</td>
          <td>0.000185</td>
          <td>0.000042</td>
          <td>0.000025</td>
          <td>0.000023</td>
          <td>predict</td>
          <td>0.000022</td>
          <td>0.000022</td>
          <td>100</td>
          <td>0.000058</td>
        </tr>
        <tr>
          <th>2</th>
          <td>1</td>
          <td>100</td>
          <td>1</td>
          <td>True</td>
          <td>skl</td>
          <td>0.000028</td>
          <td>0.000121</td>
          <td>0.000083</td>
          <td>0.000051</td>
          <td>0.000047</td>
          <td>predict_proba</td>
          <td>0.000046</td>
          <td>0.000046</td>
          <td>100</td>
          <td>0.000074</td>
        </tr>
        <tr>
          <th>3</th>
          <td>1</td>
          <td>100</td>
          <td>1</td>
          <td>True</td>
          <td>ort</td>
          <td>0.000014</td>
          <td>0.000062</td>
          <td>0.000045</td>
          <td>0.000026</td>
          <td>0.000024</td>
          <td>predict_proba</td>
          <td>0.000023</td>
          <td>0.000024</td>
          <td>100</td>
          <td>0.000037</td>
        </tr>
        <tr>
          <th>4</th>
          <td>10</td>
          <td>100</td>
          <td>1</td>
          <td>True</td>
          <td>skl</td>
          <td>0.000020</td>
          <td>0.000111</td>
          <td>0.000077</td>
          <td>0.000043</td>
          <td>0.000039</td>
          <td>predict</td>
          <td>0.000038</td>
          <td>0.000039</td>
          <td>100</td>
          <td>0.000066</td>
        </tr>
      </tbody>
    </table>
    </div>



.. code:: ipython3

    from pymlbenchmark.plotting import plot_bench_results
    plot_bench_results(df, row_cols='N', col_cols='method',
                       x_value='dim', hue_cols='fit_intercept',
                       title="LogisticRegression\nBenchmark scikit-learn / onnxruntime");



.. image:: context_8_0.png