:orphan:

.. _chart_gallery.elbow:

==============
Elbow Curve
==============

.. Necessary Code Elements

.. ipython:: python
    :suppress:
    :okwarning:

    # importing dataset and elbow function
    from vastorbit.datasets import load_iris
    from vastorbit.machine_learning.model_selection import elbow

    # creating VastFrame
    data = load_iris()


General
-------

vastorbit's Elbow Curve tool is a valuable asset for optimizing clustering models, 
particularly those employing algorithms like K-Means. This tool allows users 
to assess the performance of the clustering algorithm by plotting the distortion 
(inertia) against varying numbers of clusters (k values). Analyzing the Elbow 
Curve helps data analysts pinpoint the optimal number of clusters where the rate 
of decrease in distortion slows down, resembling the bend in an "elbow". 
This information is crucial for selecting an appropriate value for the number 
of clusters in a dataset. By leveraging the insights gained from the Elbow Curve, 
data analysts can enhance the efficiency of their clustering models, avoid 
overfitting or underfitting, and make informed decisions to achieve optimal model performance.
Let's begin by importing ```vastorbit```.



Let's use the iris dataset from ``vastorbit.datasets`` for this example.

.. code-block:: python
        
    from vastorbit.datasets import load_iris

    data = load_iris()
    


In the context of data visualization, we have the flexibility to harness multiple 
plotting libraries to craft a wide range of graphical representations. vastorbit, 
as a versatile tool, provides support for several graphic libraries, such as 
Matplotlib and Plotly. Each of these libraries offers unique features 
and capabilities, allowing us to choose the most suitable one for our specific data 
visualization needs.

.. image:: ../../docs/source/_static/plotting_libs.png
   :width: 80%
   :align: center

.. note::
    
    To select the desired plotting library, we simply need to use the `:py:func:`~vastorbit.set_option` 
    function. vastorbit offers the flexibility to smoothly transition between 
    different plotting libraries. In instances where a particular graphic is 
    not supported by the chosen library or is not supported within the vastorbit 
    framework, the tool will automatically generate a warning and then switch 
    to an alternative library where the graphic can be created.

Please click on the tabs to view the various graphics generated by the different 
plotting libraries.

.. ipython:: python
    :suppress:

    import vastorbit as vo

.. tab:: Plotly

    .. ipython:: python
        :suppress:

        vo.set_option("plotting_lib", "plotly")

    We can switch to using the ``plotly`` module.

    .. code-block:: python
        
        vo.set_option("plotting_lib", "plotly")

    Let's generate the Validation Curve.

    .. code-block:: python

        elbow(
            input_relation = data,
            X = ["PetalLengthCm", "PetalWidthCm"]
        )

    .. ipython:: python
        :suppress:
        :okwarning:
      
        fig = elbow(
            input_relation = data,
            X = ["PetalLengthCm", "PetalWidthCm"]
        )
        fig.write_html("figures/plotting_plotly_elbow_curve.html")

    .. raw:: html
        :file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_elbow_curve.html
        
.. tab:: Matplotlib

    .. ipython:: python
        :suppress:

        vo.set_option("plotting_lib", "matplotlib")

    We can switch to using the ``matplotlib`` module.

    .. code-block:: python
        
        vo.set_option("plotting_lib", "matplotlib")

    Let's generate the Validation Curve.

    .. ipython:: python
       :okwarning:

        @savefig plotting_matplotlib_elbow_curve.png
        elbow(
            input_relation = data,
            X = ["PetalLengthCm", "PetalWidthCm"]
        )
        
___________________


Chart Customization
-------------------

vastorbit empowers users with a high degree of flexibility when it comes to tailoring the visual aspects of their plots. 
This customization extends to essential elements such as **color schemes**, **text labels**, and **plot sizes**, as well as a wide range of other attributes that can be fine-tuned to align with specific design preferences and analytical requirements. Whether you want to make your visualizations more visually appealing or need to convey specific insights with precision, vastorbit's customization options enable you to craft graphics that suit your exact needs.

.. ipython:: python
   :suppress:

   from vastorbit._utils._sql._sys import purge_memory
   purge_memory()